Reading Time: 9 minutes In marketing, data isn’t a buzzword. It’s the lifeblood of all successful campaigns. But are you truly harnessing its power, or are you drowning in a sea of information? To answer this question, we sat down with Ankur Kothari, a seasoned Martech expert, to dive deep into this crucial topic. This interview, originally conducted for Chapter 6 of “The Customer Engagement Book: Adapt or Die” explores how businesses can translate raw data into actionable insights that drive real results. Ankur shares his wealth of knowledge on identifying valuable customer engagement data, distinguishing between signal and noise, and ultimately, shaping real-time strategies that keep companies ahead of the curve.   Ankur Kothari Q&A Interview 1. What types of customer engagement data are most valuable for making strategic business decisions? Primarily, there are four different buckets of customer engagement data. I would begin with behavioral data, encompassing website interaction, purchase history, and other app usage patterns. Second would be demographic information: age, location, income, and other relevant personal characteristics. Third would be sentiment analysis, where we derive information from social media interaction, customer feedback, or other customer reviews. Fourth would be the customer journey data. We track touchpoints across various channels of the customers to understand the customer journey path and conversion. Combining these four primary sources helps us understand the engagement data. 2. How do you distinguish between data that is actionable versus data that is just noise? First is keeping relevant to your business objectives, making actionable data that directly relates to your specific goals or KPIs, and then taking help from statistical significance. Actionable data shows clear patterns or trends that are statistically valid, whereas other data consists of random fluctuations or outliers, which may not be what you are interested in. You also want to make sure that there is consistency across sources. Actionable insights are typically corroborated by multiple data points or channels, while other data or noise can be more isolated and contradictory. Actionable data suggests clear opportunities for improvement or decision making, whereas noise does not lead to meaningful actions or changes in strategy. By applying these criteria, I can effectively filter out the noise and focus on data that delivers or drives valuable business decisions. 3. How can customer engagement data be used to identify and prioritize new business opportunities? First, it helps us to uncover unmet needs. By analyzing the customer feedback, touch points, support interactions, or usage patterns, we can identify the gaps in our current offerings or areas where customers are experiencing pain points. Second would be identifying emerging needs. Monitoring changes in customer behavior or preferences over time can reveal new market trends or shifts in demand, allowing my company to adapt their products or services accordingly. Third would be segmentation analysis. Detailed customer data analysis enables us to identify unserved or underserved segments or niche markets that may represent untapped opportunities for growth or expansion into newer areas and new geographies. Last is to build competitive differentiation. Engagement data can highlight where our companies outperform competitors, helping us to prioritize opportunities that leverage existing strengths and unique selling propositions. 4. Can you share an example of where data insights directly influenced a critical decision? I will share an example from my previous organization at one of the financial services where we were very data-driven, which made a major impact on our critical decision regarding our credit card offerings. We analyzed the customer engagement data, and we discovered that a large segment of our millennial customers were underutilizing our traditional credit cards but showed high engagement with mobile payment platforms. That insight led us to develop and launch our first digital credit card product with enhanced mobile features and rewards tailored to the millennial spending habits. Since we had access to a lot of transactional data as well, we were able to build a financial product which met that specific segment’s needs. That data-driven decision resulted in a 40% increase in our new credit card applications from this demographic within the first quarter of the launch. Subsequently, our market share improved in that specific segment, which was very crucial. 5. Are there any other examples of ways that you see customer engagement data being able to shape marketing strategy in real time? When it comes to using the engagement data in real-time, we do quite a few things. In the recent past two, three years, we are using that for dynamic content personalization, adjusting the website content, email messaging, or ad creative based on real-time user behavior and preferences. We automate campaign optimization using specific AI-driven tools to continuously analyze performance metrics and automatically reallocate the budget to top-performing channels or ad segments. Then we also build responsive social media engagement platforms like monitoring social media sentiments and trending topics to quickly adapt the messaging and create timely and relevant content. With one-on-one personalization, we do a lot of A/B testing as part of the overall rapid testing and market elements like subject lines, CTAs, and building various successful variants of the campaigns. 6. How are you doing the 1:1 personalization? We have advanced CDP systems, and we are tracking each customer’s behavior in real-time. So the moment they move to different channels, we know what the context is, what the relevance is, and the recent interaction points, so we can cater the right offer. So for example, if you looked at a certain offer on the website and you came from Google, and then the next day you walk into an in-person interaction, our agent will already know that you were looking at that offer. That gives our customer or potential customer more one-to-one personalization instead of just segment-based or bulk interaction kind of experience. We have a huge team of data scientists, data analysts, and AI model creators who help us to analyze big volumes of data and bring the right insights to our marketing and sales team so that they can provide the right experience to our customers. 7. What role does customer engagement data play in influencing cross-functional decisions, such as with product development, sales, and customer service? Primarily with product development — we have different products, not just the financial products or products whichever organizations sell, but also various products like mobile apps or websites they use for transactions. So that kind of product development gets improved. The engagement data helps our sales and marketing teams create more targeted campaigns, optimize channel selection, and refine messaging to resonate with specific customer segments. Customer service also gets helped by anticipating common issues, personalizing support interactions over the phone or email or chat, and proactively addressing potential problems, leading to improved customer satisfaction and retention. So in general, cross-functional application of engagement improves the customer-centric approach throughout the organization. 8. What do you think some of the main challenges marketers face when trying to translate customer engagement data into actionable business insights? I think the huge amount of data we are dealing with. As we are getting more digitally savvy and most of the customers are moving to digital channels, we are getting a lot of data, and that sheer volume of data can be overwhelming, making it very difficult to identify truly meaningful patterns and insights. Because of the huge data overload, we create data silos in this process, so information often exists in separate systems across different departments. We are not able to build a holistic view of customer engagement. Because of data silos and overload of data, data quality issues appear. There is inconsistency, and inaccurate data can lead to incorrect insights or poor decision-making. Quality issues could also be due to the wrong format of the data, or the data is stale and no longer relevant. As we are growing and adding more people to help us understand customer engagement, I’ve also noticed that technical folks, especially data scientists and data analysts, lack skills to properly interpret the data or apply data insights effectively. So there’s a lack of understanding of marketing and sales as domains. It’s a huge effort and can take a lot of investment. Not being able to calculate the ROI of your overall investment is a big challenge that many organizations are facing. 9. Why do you think the analysts don’t have the business acumen to properly do more than analyze the data? If people do not have the right idea of why we are collecting this data, we collect a lot of noise, and that brings in huge volumes of data. If you cannot stop that from step one—not bringing noise into the data system—that cannot be done by just technical folks or people who do not have business knowledge. Business people do not know everything about what data is being collected from which source and what data they need. It’s a gap between business domain knowledge, specifically marketing and sales needs, and technical folks who don’t have a lot of exposure to that side. Similarly, marketing business people do not have much exposure to the technical side — what’s possible to do with data, how much effort it takes, what’s relevant versus not relevant, and how to prioritize which data sources will be most important. 10. Do you have any suggestions for how this can be overcome, or have you seen it in action where it has been solved before? First, cross-functional training: training different roles to help them understand why we’re doing this and what the business goals are, giving technical people exposure to what marketing and sales teams do. And giving business folks exposure to the technology side through training on different tools, strategies, and the roadmap of data integrations. The second is helping teams work more collaboratively. So it’s not like the technology team works in a silo and comes back when their work is done, and then marketing and sales teams act upon it. Now we’re making it more like one team. You work together so that you can complement each other, and we have a better strategy from day one. 11. How do you address skepticism or resistance from stakeholders when presenting data-driven recommendations? We present clear business cases where we demonstrate how data-driven recommendations can directly align with business objectives and potential ROI. We build compelling visualizations, easy-to-understand charts and graphs that clearly illustrate the insights and the implications for business goals. We also do a lot of POCs and pilot projects with small-scale implementations to showcase tangible results and build confidence in the data-driven approach throughout the organization. 12. What technologies or tools have you found most effective for gathering and analyzing customer engagement data? I’ve found that Customer Data Platforms help us unify customer data from various sources, providing a comprehensive view of customer interactions across touch points. Having advanced analytics platforms — tools with AI and machine learning capabilities that can process large volumes of data and uncover complex patterns and insights — is a great value to us. We always use, or many organizations use, marketing automation systems to improve marketing team productivity, helping us track and analyze customer interactions across multiple channels. Another thing is social media listening tools, wherever your brand is mentioned or you want to measure customer sentiment over social media, or track the engagement of your campaigns across social media platforms. Last is web analytical tools, which provide detailed insights into your website visitors’ behaviors and engagement metrics, for browser apps, small browser apps, various devices, and mobile apps. 13. How do you ensure data quality and consistency across multiple channels to make these informed decisions? We established clear guidelines for data collection, storage, and usage across all channels to maintain consistency. Then we use data integration platforms — tools that consolidate data from various sources into a single unified view, reducing discrepancies and inconsistencies. While we collect data from different sources, we clean the data so it becomes cleaner with every stage of processing. We also conduct regular data audits — performing periodic checks to identify and rectify data quality issues, ensuring accuracy and reliability of information. We also deploy standardized data formats. On top of that, we have various automated data cleansing tools, specific software to detect and correct data errors, redundancies, duplicates, and inconsistencies in data sets automatically. 14. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years? The first thing that’s been the biggest trend from the past two years is AI-driven decision making, which I think will become more prevalent, with advanced algorithms processing vast amounts of engagement data in real-time to inform strategic choices. Somewhat related to this is predictive analytics, which will play an even larger role, enabling businesses to anticipate customer needs and market trends with more accuracy and better predictive capabilities. We also touched upon hyper-personalization. We are all trying to strive toward more hyper-personalization at scale, which is more one-on-one personalization, as we are increasingly capturing more engagement data and have bigger systems and infrastructure to support processing those large volumes of data so we can achieve those hyper-personalization use cases. As the world is collecting more data, privacy concerns and regulations come into play. I believe in the next few years there will be more innovation toward how businesses can collect data ethically and what the usage practices are, leading to more transparent and consent-based engagement data strategies. And lastly, I think about the integration of engagement data, which is always a big challenge. I believe as we’re solving those integration challenges, we are adding more and more complex data sources to the picture. So I think there will need to be more innovation or sophistication brought into data integration strategies, which will help us take a truly customer-centric approach to strategy formulation.   This interview Q&A was hosted with Ankur Kothari, a previous Martech Executive, for Chapter 6 of The Customer Engagement Book: Adapt or Die. Download the PDF or request a physical copy of the book here. The post Ankur Kothari Q&A: Customer Engagement Book Interview appeared first on MoEngage. #ankur #kothari #qampampa #customer #engagement

Modern healthcare innovations span AI, devices, software, images, and regulatory frameworks, all requiring stringent coordination. Generative AI arguably has the strongest transformative potential in healthcare technology programmes, with it already being applied across various domains, such as R&D, commercial operations, and supply chain management.Traditional models for medical appointments, like face-to-face appointments, and paper-based processes may not be sufficient to meet the fast-paced, data-driven medical landscape of today. Therefore, healthcare professionals and patients are seeking more convenient and efficient ways to access and share information, meeting the complex standards of modern medical science. According to McKinsey, Medtech companies are at the forefront of healthcare innovation, estimating they could capture between billion and billion annually in productivity gains. Through GenAI adoption, an additional billion plus in revenue is estimated from products and service innovations. A McKinsey 2024 survey revealed around two thirds of Medtech executives have already implemented Gen AI, with approximately 20% scaling their solutions up and reporting substantial benefits to productivity.  While advanced technology implementation is growing across the medical industry, challenges persist. Organisations face hurdles like data integration issues, decentralised strategies, and skill gaps. Together, these highlight a need for a more streamlined approach to Gen AI deployment. Of all the Medtech domains, R&D is leading the way in Gen AI adoption. Being the most comfortable with new technologies, R&D departments use Gen AI tools to streamline work processes, such as summarising research papers or scientific articles, highlighting a grassroots adoption trend. Individual researchers are using AI to enhance productivity, even when no formal company-wide strategies are in place.While AI tools automate and accelerate R&D tasks, human review is still required to ensure final submissions are correct and satisfactory. Gen AI is proving to reduce time spent on administrative tasks for teams and improve research accuracy and depth, with some companies experiencing 20% to 30% gains in research productivity. KPIs for success in healthcare product programmesMeasuring business performance is essential in the healthcare sector. The number one goal is, of course, to deliver high-quality care, yet simultaneously maintain efficient operations. By measuring and analysing KPIs, healthcare providers are in a better position to improve patient outcomes through their data-based considerations. KPIs can also improve resource allocation, and encourage continuous improvement in all areas of care. In terms of healthcare product programmes, these structured initiatives prioritise the development, delivery, and continual optimisation of medical products. But to be a success, they require cross-functional coordination of clinical, technical, regulatory, and business teams. Time to market is critical, ensuring a product moves from the concept stage to launch as quickly as possible.Of particular note is the emphasis needing to be placed on labelling and documentation. McKinsey notes that AI-assisted labelling has resulted in a 20%-30% improvement in operational efficiency. Resource utilisation rates are also important, showing how efficiently time, budget, and/or headcount are used during the developmental stage of products. In the healthcare sector, KPIs ought to focus on several factors, including operational efficiency, patient outcomes, financial health of the business, and patient satisfaction. To achieve a comprehensive view of performance, these can be categorised into financial, operational, clinical quality, and patient experience.Bridging user experience with technical precision – design awardsInnovation is no longer solely judged by technical performance with user experiencebeing equally important. Some of the latest innovations in healthcare are recognised at the UX Design Awards, products that exemplify the best in user experience as well as technical precision. Top products prioritise the needs and experiences of both patients and healthcare professionals, also ensuring each product meets the rigorous clinical and regulatory standards of the sector. One example is the CIARTIC Move by Siemens Healthineers, a self-driving 3D C-arm imaging system that lets surgeons operate, controlling the device wirelessly in a sterile field. Computer hardware company ASUS has also received accolades for its HealthConnect App and VivoWatch Series, showcasing the fusion of AIoT-driven smart healthcare solutions with user-friendly interfaces – sometimes in what are essentially consumer devices. This demonstrates how technical innovation is being made accessible and becoming increasingly intuitive as patients gain technical fluency.  Navigating regulatory and product development pathways simultaneously The establishing of clinical and regulatory paths is important, as this enables healthcare teams to feed a twin stream of findings back into development. Gen AI adoption has become a transformative approach, automating the production and refining of complex documents, mixed data sets, and structured and unstructured data. By integrating regulatory considerations early and adopting technologies like Gen AI as part of agile practices, healthcare product programmes help teams navigate a regulatory landscape that can often shift. Baking a regulatory mindset into a team early helps ensure compliance and continued innovation. Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.Explore other upcoming enterprise technology events and webinars powered by TechForge here. #medtech #hardware #clinical #application #programmes

Reading Time: 9 minutes In the ever-evolving landscape of customer engagement, staying ahead of the curve is not just advantageous, it’s essential. That’s why, for Chapter 7 of “The Customer Engagement Book: Adapt or Die,” we sat down with Mirela Cialai, a seasoned expert in CRM and Martech strategies at brands like Equinox. Mirela brings a wealth of knowledge in aligning technology roadmaps with business goals, shifting organizational focuses from acquisition to retention, and leveraging hyper-personalization to drive success. In this interview, Mirela dives deep into building robust customer engagement technology roadmaps. She unveils the “PAPER” framework—Plan, Audit, Prioritize, Execute, Refine—a simple yet effective strategy for marketers. You’ll gain insights into identifying gaps in your Martech stack, ensuring data accuracy, and prioritizing initiatives that deliver the greatest impact and ROI. Whether you’re navigating data silos, striving for cross-functional alignment, or aiming for seamless tech integration, Mirela’s expertise provides practical solutions and actionable takeaways.   Mirela Cialai Q&A Interview 1. How do you define the vision for a customer engagement platform roadmap in alignment with the broader business goals? Can you share any examples of successful visions from your experience? Defining the vision for the roadmap in alignment with the broader business goals involves creating a strategic framework that connects the team’s objectives with the organization’s overarching mission or primary objectives. This could be revenue growth, customer retention, market expansion, or operational efficiency. We then break down these goals into actionable areas where the team can contribute, such as improving engagement, increasing lifetime value, or driving acquisition. We articulate how the team will support business goals by defining the KPIs that link CRM outcomes — the team’s outcomes — to business goals. In a previous role, the CRM team I was leading faced significant challenges due to the lack of attribution capabilities and a reliance on surface-level metrics such as open rates and click-through rates to measure performance. This approach made it difficult to quantify the impact of our efforts on broader business objectives such as revenue growth. Recognizing this gap, I worked on defining a vision for the CRM team to address these shortcomings. Our vision was to drive measurable growth through enhanced data accuracy and improved attribution capabilities, which allowed us to deliver targeted, data-driven, and personalized customer experiences. To bring this vision to life, I developed a roadmap that focused on first improving data accuracy, building our attribution capabilities, and delivering personalization at scale. By aligning the vision with these strategic priorities, we were able to demonstrate the tangible impact of our efforts on the key business goals. 2. What steps did you take to ensure data accuracy? The data team was very diligent in ensuring that our data warehouse had accurate data. So taking that as the source of truth, we started cleaning the data in all the other platforms that were integrated with our data warehouse — our CRM platform, our attribution analytics platform, etc. That’s where we started, looking at all the different integrations and ensuring that the data flows were correct and that we had all the right flows in place. And also validating and cleaning our email database — that helped, having more accurate data. 3. How do you recommend shifting organizational focus from acquisition to retention within a customer engagement strategy? Shifting an organization’s focus from acquisition to retention requires a cultural and strategic shift, emphasizing the immense value that existing customers bring to long-term growth and profitability. I would start by quantifying the value of retention, showcasing how retaining customers is significantly more cost-effective than acquiring new ones. Research consistently shows that increasing retention rates by just 5% can boost profits by at least 25 to 95%. This data helps make a compelling case to stakeholders about the importance of prioritizing retention. Next, I would link retention to core business goals by demonstrating how enhancing customer lifetime value and loyalty can directly drive revenue growth. This involves shifting the organization’s focus to retention-specific metrics such as churn rate, repeat purchase rate, and customer LTV. These metrics provide actionable insights into customer behaviors and highlight the financial impact of retention initiatives, ensuring alignment with the broader company objectives. By framing retention as a driver of sustainable growth, the organization can see it not as a competing priority, but as a complementary strategy to acquisition, ultimately leading to a more balanced and effective customer engagement strategy. 4. What are the key steps in analyzing a brand’s current Martech stack capabilities to identify gaps and opportunities for improvement? Developing a clear understanding of the Martech stack’s current state and ensuring it aligns with a brand’s strategic needs and future goals requires a structured and strategic approach. The process begins with defining what success looks like in terms of technology capabilities such as scalability, integration, automation, and data accessibility, and linking these capabilities directly to the brand’s broader business objectives. I start by doing an inventory of all tools currently in use, including their purpose, owner, and key functionalities, assessing if these tools are being used to their full potential or if there are features that remain unused, and reviewing how well tools integrate with one another and with our core systems, the data warehouse. Also, comparing the capabilities of each tool and results against industry standards and competitor practices and looking for missing functionalities such as personalization, omnichannel orchestration, or advanced analytics, and identifying overlapping tools that could be consolidated to save costs and streamline workflows. Finally, review the costs of the current tools against their impact on business outcomes and identify technologies that could reduce costs, increase efficiency, or deliver higher ROI through enhanced capabilities. Establish a regular review cycle for the Martech stack to ensure it evolves alongside the business and the technological landscape. 5. How do you evaluate whether a company’s tech stack can support innovative customer-focused campaigns, and what red flags should marketers look out for? I recommend taking a structured approach and first ensure there is seamless integration across all tools to support a unified customer view and data sharing across the different channels. Determine if the stack can handle increasing data volumes, larger audiences, and additional channels as the campaigns grow, and check if it supports dynamic content, behavior-based triggers, and advanced segmentation and can process and act on data in real time through emerging technologies like AI/ML predictive analytics to enable marketers to launch responsive and timely campaigns. Most importantly, we need to ensure that the stack offers robust reporting tools that provide actionable insights, allowing teams to track performance and optimize campaigns. Some of the red flags are: data silos where customer data is fragmented across platforms and not easily accessible or integrated, inability to process or respond to customer behavior in real time, a reliance on manual intervention for tasks like segmentation, data extraction, campaign deployment, and poor scalability. If the stack struggles with growing data volumes or expanding to new channels, it won’t support the company’s evolving needs. 6. What role do hyper-personalization and timely communication play in a successful customer engagement strategy? How do you ensure they’re built into the technology roadmap? Hyper-personalization and timely communication are essential components of a successful customer engagement strategy because they create meaningful, relevant, and impactful experiences that deepen the relationship with customers, enhance loyalty, and drive business outcomes. Hyper-personalization leverages data to deliver tailored content that resonates with each individual based on their preferences, behavior, or past interactions, and timely communication ensures these personalized interactions occur at the most relevant moments, which ultimately increases their impact. Customers are more likely to engage with messages that feel relevant and align with their needs, and real-time triggers such as cart abandonment or post-purchase upsells capitalize on moments when customers are most likely to convert. By embedding these capabilities into the roadmap through data integration, AI-driven insights, automation, and continuous optimization, we can deliver impactful, relevant, and timely experiences that foster deeper customer relationships and drive long-term success. 7. What’s your approach to breaking down the customer engagement technology roadmap into manageable phases? How do you prioritize the initiatives? To create a manageable roadmap, we need to divide it into distinct phases, starting with building the foundation by addressing data cleanup, system integrations, and establishing metrics, which lays the groundwork for success. Next, we can focus on early wins and quick impact by launching behavior-based campaigns, automating workflows, and improving personalization to drive immediate value. Then we can move to optimization and expansion, incorporating predictive analytics, cross-channel orchestration, and refined attribution models to enhance our capabilities. Finally, prioritize innovation and scalability, leveraging AI/ML for hyper-personalization, scaling campaigns to new markets, and ensuring the system is equipped for future growth. By starting with foundational projects, delivering quick wins, and building towards scalable innovation, we can drive measurable outcomes while maintaining our agility to adapt to evolving needs. In terms of prioritizing initiatives effectively, I would focus on projects that deliver the greatest impact on business goals, on customer experience and ROI, while we consider feasibility, urgency, and resource availability. In the past, I’ve used frameworks like Impact Effort Matrix to identify the high-impact, low-effort initiatives and ensure that the most critical projects are addressed first. 8. How do you ensure cross-functional alignment around this roadmap? What processes have worked best for you? Ensuring cross-functional alignment requires clear communication, collaborative planning, and shared accountability. We need to establish a shared understanding of the roadmap’s purpose and how it ties to the company’s overall goals by clearly articulating the “why” behind the roadmap and how each team can contribute to its success. To foster buy-in and ensure the roadmap reflects diverse perspectives and needs, we need to involve all stakeholders early on during the roadmap development and clearly outline each team’s role in executing the roadmap to ensure accountability across the different teams. To keep teams informed and aligned, we use meetings such as roadmap kickoff sessions and regular check-ins to share updates, address challenges collaboratively, and celebrate milestones together. 9. If you were to outline a simple framework for marketers to follow when building a customer engagement technology roadmap, what would it look like? A simple framework for marketers to follow when building the roadmap can be summarized in five clear steps: Plan, Audit, Prioritize, Execute, and Refine. In one word: PAPER. Here’s how it breaks down. Plan: We lay the groundwork for the roadmap by defining the CRM strategy and aligning it with the business goals. Audit: We evaluate the current state of our CRM capabilities. We conduct a comprehensive assessment of our tools, our data, the processes, and team workflows to identify any potential gaps. Prioritize: initiatives based on impact, feasibility, and ROI potential. Execute: by implementing the roadmap in manageable phases. Refine: by continuously improving CRM performance and refining the roadmap. So the PAPER framework — Plan, Audit, Prioritize, Execute, and Refine — provides a structured, iterative approach allowing marketers to create a scalable and impactful customer engagement strategy. 10. What are the most common challenges marketers face in creating or executing a customer engagement strategy, and how can they address these effectively? The most critical is when the customer data is siloed across different tools and platforms, making it very difficult to get a unified view of the customer. This limits the ability to deliver personalized and consistent experiences. The solution is to invest in tools that can centralize data from all touchpoints and ensure seamless integration between different platforms to create a single source of truth. Another challenge is the lack of clear metrics and ROI measurement and the inability to connect engagement efforts to tangible business outcomes, making it very hard to justify investment or optimize strategies. The solution for that is to define clear KPIs at the outset and use attribution models to link customer interactions to revenue and other key outcomes. Overcoming internal silos is another challenge where there is misalignment between teams, which can lead to inconsistent messaging and delayed execution. A solution to this is to foster cross-functional collaboration through shared goals, regular communication, and joint planning sessions. Besides these, other challenges marketers can face are delivering personalization at scale, keeping up with changing customer expectations, resource and budget constraints, resistance to change, and others. While creating and executing a customer engagement strategy can be challenging, these obstacles can be addressed through strategic planning, leveraging the right tools, fostering collaboration, and staying adaptable to customer needs and industry trends. By tackling these challenges proactively, marketers can deliver impactful customer-centric strategies that drive long-term success. 11. What are the top takeaways or lessons that you’ve learned from building customer engagement technology roadmaps that others should keep in mind? I would say one of the most important takeaways is to ensure that the roadmap directly supports the company’s broader objectives. Whether the focus is on retention, customer lifetime value, or revenue growth, the roadmap must bridge the gap between high-level business goals and actionable initiatives. Another important lesson: The roadmap is only as effective as the data and systems it’s built upon. I’ve learned the importance of prioritizing foundational elements like data cleanup, integrations, and governance before tackling advanced initiatives like personalization or predictive analytics. Skipping this step can lead to inefficiencies or missed opportunities later on. A Customer Engagement Roadmap is a strategic tool that evolves alongside the business and its customers. So by aligning with business goals, building a solid foundation, focusing on impact, fostering collaboration, and remaining adaptable, you can create a roadmap that delivers measurable results and meaningful customer experiences.     This interview Q&A was hosted with Mirela Cialai, Director of CRM & MarTech at Equinox, for Chapter 7 of The Customer Engagement Book: Adapt or Die. Download the PDF or request a physical copy of the book here. The post Mirela Cialai Q&A: Customer Engagement Book Interview appeared first on MoEngage. #mirela #cialai #qampampa #customer #engagement

On-device channels are no longer all about preloads. Today, telcos represent another performance marketing channel with transparent reporting and deeper insights. To get the full picture behind the performance of your on-device campaigns, it’s critical to prioritize long-term KPIs. It’s the only way the stickiness of users acquired through these channels really shine. Why?On-device campaigns reach users when they’re setting up their new devices and looking to download apps they’ll use throughout the device lifetime, not necessarily right away. Think about it - if you download a booking app from an ad during device setup, are you planning to book a vacation immediately or later down the road?This means attribution is a waiting game for on-device campaigns, with day 30 as the turning point. In fact, if a user engages with your app 30 days down the line, they’re more likely to stay active for a long period of time. Simply put, LTV is high for on-device campaigns. This means you want to be looking at KPIs that allow you to measure and optimize the value of the users you attract far down the road.ROASROAS is king when it comes to measuring the long-term value of your users. To get the clearest idea of your ROAS and how to optimize it, there are a few things to keep in mind. First, ROAS should be measured on D30/60/90 not D1/3/7. This is because, with on-device channels, users are likely to open an app within the first 30 days or longer - when a user downloads an app during device setup, they do so expecting to open it in the future, not right away.You should also pay attention to how it’s being measured. ROAS is calculated by dividing the amount of revenue a campaign generates by the amount it costs to run it. In the context of on-device campaigns, that revenue comes from in-app purchases, subscriptions, or ad monetization.When measuring the effectiveness of your on-device campaigns, it’s important to calculate ROAS using your on-device ad revenue rather than average ad revenue, which will be lower. That’s because ad revenue is high for users acquired through on-device campaigns - on-device channels use unique data points and deep algorithms to ensure the right bid for each individual user. To get the clearest picture of where you stand in relation to your ROAS goals, you should integrate ad revenue with your on-device platform.Once calculated, ROAS gives a clear monetary view of your campaigns, so it’s clear how much you spent vs brought in. This monetary value is important because it tells you if your on-device campaigns are reaching valuable users. Looking at ROAS by placements, you get insight into which placements are doing it best. With the knowledge of how to maximize ROAS, you’ll maximize the long term value and engagement of your users, too.Cost KPIsComparing LTV to spend will help you determine whether or not your users are spending enough to cover your spend and ultimately turn a profit. You can even pinpoint areas of your strategy that are effective, and those that may need adjustment.There are a few ways to measure cost effectiveness. Here are the most common two, especially for on-device campaigns.Cost per actionIf it’s quality you’re looking for, first, run a CPA campaign to confirm that you’re looking in the right places for users who will engage with your app. To count as a conversion, users must see the ad, install the app, and complete the action you preset. You’ll only pay for the users who reach a chosen point in the app experience after installation. A CPA that is higher than LTV is a clear indicator that your campaigns are focused on less relevant channels or touchpoints, while a CPA that is lower than your LTV confirms that you are attracting high quality users.In the context of on-device campaigns, this is key because it means you won't pay immediately for a user who may not engage for a month or so. The pricing model also integrates in-app revenue, which is useful for apps that rely more on IAPs than ads.Cost per retained userIt’s also worthwhile to keep track of how much you’re paying for the user that’s still there on day 30. CPRU takes into account conversions and retention rate - if your budget is k, you have 1000 conversions and a day 1 retention rate of 20%, you come away with 200 converted users at a per user acquisition cost. If you can increase retention, you end up with higher quality users at a lower CPRU.Measuring CPRU, retention becomes a success metric for your UA campaigns and can help you determine whether you have enough engaged users to cover spend.On day 30 and beyond, these KPIs can help you optimize your on-device campaigns to reach the most engaged users with high LTV. #most #important #kpis #running #ondevice

John Edwards, Technology Journalist & AuthorMay 30, 20254 Min ReadTanapong Sungkaew via Alamy Stock PhotoAI adoption can help enterprises function more efficiently and productively in many internal and external areas. Yet to get the most value out of AI, CIOs and IT leaders need to find a way to measure their current and future gains.Measuring AI efficiency and productivity gains isn't always a straightforward process, however, observes Matt Sanchez, vice president of product for IBM's watsonx Orchestrate, a tool designed to automate tasks, focusing on the orchestration of AI assistants and AI agents."There are many factors to consider in order to gain an accurate picture of AI’s impact on your organization," Sanchez says,  in an email interview. He believes the key to measuring AI effectiveness starts with setting clear, data-driven goals. "What outcomes are you trying to achieve?" he asks. "Identifying the right key performance indicators -- KPIs -- that align with your overall strategy is a great place to start."Measuring AI efficiency is a little like a "chicken or the egg" discussion, says Tim Gaus, smart manufacturing business leader at Deloitte Consulting. "A prerequisite for AI adoption is access to quality data, but data is also needed to show the adoption’s success," he advises in an online interview.Still, with the number of organizations adopting AI rapidly increasing, C-suites and boards are now prioritizing measurable ROI.Related:"We're seeing this firsthand while working with clients in the manufacturing space specifically who are aiming to make manufacturing processes smarter and increasingly software-defined," Gaus says.Measuring AI Efficiency: The ChallengeThe challenge in measuring AI efficiency depends on the type of AI and how it's ultimately used, Gaus says. Manufacturers, for example, have long used AI for predictive maintenance and quality control. "This can be easier to measure, since you can simply look at changes in breakdown or product defect frequencies," he notes. "However, for more complex AI use cases -- including using GenAI to train workers or serve as a form of knowledge retention -- it can be harder to nail down impact metrics and how they can be obtained."AI Project Measurement MethodsOnce AI projects are underway, Gaus says measuring real-world results is key. "This includes studying factors such as actual cost reductions, revenue boosts tied directly to AI, and progress in KPIs such as customer satisfaction or operational output. "This method allows organizations to track both the anticipated and actual benefits of their AI investments over time."Related:To effectively assess AI's impact on efficiency and productivity, it's important to connect AI initiatives with broader business goals and evaluate their progress at different stages, Gaus says."In the early stages, companies should focus on estimating the potential benefits, such as enhanced efficiency, revenue growth, or strategic advantages like stronger customer loyalty or reduced operational downtime." These projections can provide a clear understanding of how AI aligns with long-term objectives, Gaus adds.Measuring any emerging technology's impact on efficiency and productivity often takes time, but impacts are always among the top priorities for business leaders when evaluating any new technology, says Dan Spurling, senior vice president of product management at multi-cloud data platform provider Teradata. "Businesses should continue to use proven frameworks for measurement rather than create net-new frameworks," he advises in an online interview. "Metrics should be set prior to any investment to maximize benefits and mitigate biases, such as sunk cost fallacies, confirmation bias, anchoring bias, and the like."Key AI Value MetricsMetrics can vary depending on the industry and technology being used, Gaus says. "In sectors like manufacturing, AI value metrics include improvements in efficiency, productivity, and cost reduction." Yet specific metrics depend on the type of AI technology implemented, such as machine learning.Related:Beyond tracking metrics, it's important to ensure high-quality data is used to minimize biases in AI decision-making, Sanchez says. The end goal is for AI to support the human workforce, freeing users to focus on strategic and creative work and removing potential bottlenecks. "It's also important to remember that AI isn't a one-and-done deal. It's an ongoing process that needs regular evaluation and process adjustment as the organization transforms.”Spurling recommends beginning by studying three key metrics:Worker productivity: Understanding the value of increased task completion or reduced effort by measuring the effect on day-to-day activities like faster issue resolution, more efficient collaboration, reduced process waste, or increased output quality.Ability to scale: Operationalizing AI-based self-service tools, typically with natural language capabilities, across the entire organization beyond IT to enable task or job completion in real-time, with no need for external support or augmentation.User friendliness: Expanding organization effectiveness with data-driven insights as measured by the ability of non-technical business users to leverage AI via no-code, low-code platforms.Final Note: Aligning Business and TechnologyDeloitte's digital transformation research reveals that misalignment between business and technology leaders often leads to inaccurate ROI assessments, Gaus says. "To address this, it's crucial for both sides to agree on key value priorities and success metrics."He adds it's also important to look beyond immediate financial returns and to incorporate innovation-driven KPIs, such as experimentation toleration and agile team adoption. "Without this broader perspective, up to 20% of digital investment returns may not yield their full potential," Gaus warns. "By addressing these alignment issues and tracking a comprehensive set of metrics, organizations can maximize the value from AI initiatives while fostering long-term innovation."About the AuthorJohn EdwardsTechnology Journalist & AuthorJohn Edwards is a veteran business technology journalist. His work has appeared in The New York Times, The Washington Post, and numerous business and technology publications, including Computerworld, CFO Magazine, IBM Data Management Magazine, RFID Journal, and Electronic Design. He has also written columns for The Economist's Business Intelligence Unit and PricewaterhouseCoopers' Communications Direct. John has authored several books on business technology topics. His work began appearing online as early as 1983. Throughout the 1980s and 90s, he wrote daily news and feature articles for both the CompuServe and Prodigy online services. His "Behind the Screens" commentaries made him the world's first known professional blogger.See more from John EdwardsWebinarsMore WebinarsReportsMore ReportsNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also Like #how #measure #efficiency #productivity #gains

Influencer Strategy Specialist is a strategic and mission-critical role at Bunny Agency LLC. You will be responsible for maximizing creator virality, engagement, and brand loyalty through strategic planning, visual storytelling, and social media optimization, acting as a creative strategist and cross-functional collaborator while overseeing Instagram performance, creative direction, and marketing growth initiatives. This is not just a creative support role; it's a growth-driving leadership position where you will directly impact creator success, audience retention, and revenue growth. You will work closely with the Talent, Content, Growth, and Marketing teams to ensure every creator reaches their full potential while aligned with brand excellence.Key Responsibilities1. Creator Growth & Virality StrategyBuild tailored growth strategies to elevate individual creators across digital platforms.Audit content performance and develop virality roadmaps based on platform trends and analytics.Key Deliverables: Increase average creator reach by 40% YoY; double engagement rates for top-tier creators.2. Concept Development & Mood Board CreationIdeate and develop high-concept marketing campaigns and creative direction for influencers.Create and present visually driven mood boards that align with audience psychology and brand goals.Key Deliverables: Launch 10+ campaign-ready concepts per quarter; improve creator branding consistency by 80%.3. Instagram Strategy & Team OversightOwn and execute Instagram growth plans for both creators and internal brand presence.Lead a team of content managers, designers, and editors to deliver on high-impact Instagram initiatives.Key Deliverables: Grow followers by 100K quarterly across creator roster; maintain a 4%+ engagement rate.4. Loyalty & Audience Retention SystemsImplement strategy frameworks that build deeper fan connection and long-term creator community.Leverage CRM tools, DMs, stories, polls, and unique UGC to drive two-way loyalty.Key Deliverables: Improve audience retention metrics by 30%; design scalable fan-interaction loops.5. Cross-Team Strategy AlignmentAct as the bridge between content, marketing, and analytics to ensure strategy coherence.Regularly update leadership with insights and proposals that drive revenue through creator performance.Key Deliverables: Submit bi-weekly insights reports; increase campaign conversion rates by 25% across collaborations.RequirementsBachelor’s degree in Marketing, Digital Media, Communications, or a related field.4+ years experience in influencer marketing, digital growth strategy, or brand content strategy.Proven portfolio of viral campaigns and successful creator-led marketing initiatives.Deep knowledge of Instagram growth tactics, viral psychology, audience metrics, and creator monetization.Experience leading small teams and coordinating with design, content, and analytics departments.Proficiency in tools like Canva, Adobe Creative Suite, Notion, Meta Business Suite, and social analytics platforms.5 Benefits That Come with This JobAbove market salary and attractive incentives.We ensure that you grow both personally and professionally. Your growth is the team's number one priority.Flexible Time Off Policy & Company-wide Holidays.Work autonomously and own your projects.Sport Club Membership: We cover the cost of your gym, sports club, yoga, or any fitness activity up to 4 times a week, capped at per month.Bunnycations: Enjoy Team and Creator Events at dream destinations. 🐰Personal AttributesStrategic thinker with the ability to navigate ambiguityExcellent communication and interpersonal skillsHigh emotional intelligence and conflict resolution skillsResults-oriented with the ability to drive measurable impactStrong integrity and alignment with company core valuesResults/OutcomesCreator virality rate increased by 3x across portfolioInstagram engagement rate maintained at 4%+ for creatorsMood boards and creative strategies deliver 10+ branded campaigns/quarterClient/creator retention improved by 25% through loyalty and feedback systemsCompany Values#1 – Tough LoveHave the self-awareness to accurately perceive and communicate hard truths that improve others and self, the courage to do so, and the humility to accept them—even when it hurts. Nothing great can be built without feedback.#2 – Competitive GreatnessBe at your best when your best is needed. Embrace the challenge. Drive improvement, see the bigger picture, and build long-term excellence.#3 – Unimpeachable CharacterBe the kind of person with whom others are proud to associate. Align your thoughts, words, and actions with goals that matter.SummarySalary: €4,500–€6,000/monthOn-Track-Earnings: €70,000–€85,000/year with bonuses tied to creator KPIsPlace: RemoteHow to ApplySubmit your application via with your resume and a cover letter detailing why you're a perfect fit for this role at Bunny Agency LLC.Apply NowLet's start your dream job Apply now Meet JobCopilot: Your Personal AI Job HunterAutomatically Apply to Remote Sales and Marketing JobsJust set your preferences and Job Copilot will do the rest-finding, filtering, and applying while you focus on what matters. Activate JobCopilot #bunny #agency #llc #influencer #strategy

At this year’s Mobidictum Business Conference, Lior Shekel, Director of Strategic Partnerships at ironSource, walked through 5 steps to launch a hit hyper-casual game today - including tips for marketability testing, soft launching, and global launching. Let’s dive in.Step 1: Run a marketability test on social networksThe first step to launching a hyper-casual game is running an initial marketability test on social media networks - this tells you its relative potential for success in the market. Facebook is typically the best place to start - it has a simple integration, it’s relatively fast and cheap to use, and offers a vast audience. Essentially, testing on Facebook gives you a general idea whether your game prototype matches your KPI goals.If test results on Facebook look promising, try marketability testing on other social networks like Google, Snapchat and TikTok. This will give you even broader confirmation that your game stands out among the competition.Step 2: Run a marketability test on SDK networksOnce initial KPIs are looking good, now it’s time to test on SDK networks. Why? SDK networks offer the biggest audience possible, and the biggest opportunity for your game to scale up during the global launch. Just because a game passes marketability on Facebook, doesn’t necessarily mean it will scale on an SDK network later on.To understand marketability on SDK networks, we look at eCPM, which is IPM multiplied by CPI. Like the chart shows, the campaigns that generate the highest eCPMs will be the ones to top the SDK networks’ data science ad serving models, winning the most impressions and scaling the fastest. Essentially, the higher the eCPM, the more the purchasing power your campaign has on the network.Step 3: Implement ad monetizationOnce your game’s KPIs look promising on both social media and SDK networks, you’re almost ready for launch. But first, you need to maximize your monetization opportunities through the game content itself. This includes adding more levels, A/B testing different placement and creative strategies, determining a basic game economy, and more.This way, before launch, you’re putting yourself in the best position to profit from your game, while ensuring your users have the most positive and exciting experience possible.Step 4: Run a soft launchNow it’s time to soft launch your game - releasing the product ahead of the scheduled launch is an opportunity to simulate real-world interactions. Since you’ve already tested your marketability, you can confidently soft launch on every ad network possible to increase your buying power.Some hyper-casual studios choose to initially soft launch on social networks, then later on SDKs - but we see that the most successful cases soft launch everywhere at once. Let’s dive into two different soft launch strategies.Going all inThe first strategy, the “all-in” launch, focuses on scaling up quickly to find the profitable and scalable sweet spot. It starts with a higher-than-average bid, giving you a higher eCPM, as we mentioned earlier. By spending extra money, you’re increasing your eCPM - so you can scale quickly and top the charts, but your profit KPIs will drop as a result. To retain your revenue, we recommend capping this campaign at -1000 daily.Next, you lower your bid each day by no more than 10% - increasing the budget cap simultaneously. This process usually takes around 3 days, helping you reach your sweet spot when you can start granular bidding - carefully setting and adjusting different bids according to their source behavior. You should bid per source based on the quality that it’s generating for you, such as increasing bids in sources with a good ROAS.Staying conservativeConversely, a conservative launch is much more stable - but because it prioritizes profit, it scales much more slowly. With this strategy, you start with your target bid, which means your budget will likely be smaller, but will soon grow. At this stage, it’s crucial to prevent your game from reaching its budget cap - it harms the game’s positive trend and growth potential.To start granular bidding within 4-5 days, you should also be monitoring ROAS from day 1.Comparing four games that used one of these two strategies - every single one hit the top charts, with games using the “all-in” strategy staying there for a slightly longer time. Most importantly, despite the strategy, the games’ total revenue was virtually the same at the end of the day.Staying ahead of the curveNo matter which soft launch strategy you use, we recommend first launching in the US - it’s the biggest market offering the highest bids and eCPMs. From there, we recommend waiting 2-3 days before launching globally. To get maximum scale from your top sources, it’s best to optimize based on ROAS source bidding. Eventually, you can go on “auto-pilot” mode by turning on automated ROAS optimizer campaigns within each ad network - reducing workload for the whole team.As you global launch, keep these tips in mind:- Timing is everything - launch close to the weekend for longer retention and more playtime- Utilize your top-performing creatives and test new creatives - this improves IPM, which therefore boosts eCPM- Call your users to action using different interactive and playable end cards to increase IPMStep 5: Automate and scale upCongrats! You’ve now global launched your game, new users are joining every day, and LTV is becoming much more accurate. Now is the best time to automate user acquisition, so you and your team can not only focus on acquiring the users, but also the game itself - and you can buy users much more efficiently.Not only does automation free your time, but it also uses highly accurate data - many ROAS optimizers today bid on an extremely granular level, for example per user and per ad request. This expands your reach because you can adjust your bids all the time, while your optimizer continues to optimize to scale and profit.By launching your hyper-casual game in a thoughtful way, you’re ensuring it’s on the best path for success - from the earliest marketability testing stages, all the way through post launch and automation. #steps #launching #hypercasual #game

OverviewBacked by Craig Zingerline, Growth Minded is a boutique micro-agency focused on helping early stage through growth startups grow. We're looking for a Director of Lifecycle Marketing & Growth Strategy to help us increase our client engagement capacity, as well as work on internal projects. To learn more about Craig, please visit his Growth Mentor profile, or check him out on LinkedIn.What we doClients hire us to help them find, convert, and retain customers. We work from high level strategy down into channel level tactics, and every layer in-between. As a company and team we care deeply about solving the challenging problems our customers face, and typically own major components of the marketing side of their business. We focus first on growth strategy development, then deliver tactics through a thoughtful, experiment driven approach. Our customers trust us with their business - and we take our work seriously.The roleWe are looking for a talented growth marketing with deep experience running lifecycle marketing programs and building growth strategies. Our ideal candidate has a blend of in-house client-side experience plus agency experience, but candidates with strong lifecycle marketing and growth strategy chops will be considered regardless of exact background. We care deeply about partnering with hardworking, thoughtful, and intellectually curious individuals who are strong lifecycle marketing experts, have world class growth strategy chops, are talented at writing and content framework development, are strategic thinkers, and who have the ability to go deep into the weeds, helping execute campaigns for our clients.Strong client facing skills are a must - you must present well, be able to take feedback and think on the fly, and bring thoughtful, actionable work to the company each day.Job OverviewAs the Director of Lifecycle Marketing & Growth Strategy, you will own the growth strategy and execution of lifecycle marketing campaigns across various channels, focusing on customer acquisition, activation, retention, and monetization. You will work closely with client product, marketing, sales, and analytics teams to drive user growth, create scalable strategies, and optimize conversion pathways to help our clients achieve and exceed their growth goals.Key Responsibilities- Develop comprehensive lifecycle marketing strategies for clients- Lead client relationship management, serving as the primary strategic point of contact- Design and optimize multi-channel customer journeys to maximize engagement, conversion, and retention- Analyze customer data to identify segmentation opportunities and personalization strategies- Write, design and implement email marketing campaigns- Oversee push notification, SMS, and in-app messaging strategies- Implement automated flows with sophisticated triggers- Monitor campaign performance, conduct A/B testing, and provide data-driven recommendations- Set up reports and dashboard on performance- Monitor and identify deliverability issues, as well as implement both technical and content steps to improve deliverability- Build strategies around list cleaning and list hygiene- Ideate and implement ideas on list growth- Present campaign results and strategic insights to clients with clear ROI measurements- Stay current with industry trends and emerging technologies in lifecycle marketing- Develop client proposals and participate in new business pitchesData-Driven Decision Making:- Analyze data to understand the effectiveness of growth initiatives, measure KPIs, and make informed decisions.- Use customer segmentation and cohort analysis to uncover opportunities to increase user engagement and LTV.Nice to have's include:- Deep understanding and audit of the client’s current state of growth.- Design and execute growth strategies that cover the entire customer lifecycle, including acquisition, conversion, retention, and expansion.- Identify new growth opportunities for our clients through data analysis, customer insights, and market research.- Rank the opportunities and present evidence as to the order in which execution of the strategy should follow.- Lead A/B testing initiatives and growth experiments to improve customer acquisition and conversion rates.- Develop an experimentation framework to quickly validate ideas and scale winning initiatives.Collaboration and Leadership- Collaborate with both internal and client teams across marketing, product, and sales to ensure alignment in growth efforts.- Act as an owner within our small company, doing what it takes to help our clients succeed.-Ability to diplomatically assess tradeoffs between competing objectives.Requirements-Proven experience in a growth role, preferably at a startup or fast-paced environment within an agency.-Strong understanding of growth metrics and KPIs, including CAC, LTV, churn, and conversion rates.-Deep experience with lifecycle marketing-Deep experience with one or more of the following platforms: Kit, Hubspot, Salesforce, Klaviyo, Iterable, Customer.io, Google Tag Manager, Google Analytics, etc.- STRONG copywriting and content skills. Almost everything we do requires great content. We have an in-house editor that you can leverage but you'll need to be leading efforts on content for multiple clients. - Deep interest and/or experience in leveraging AI tools to boost your productivity. We are a high output company even though we're small in size. We prefer candidates who are already deeply leveraging AI, but at a minimum, you should have the desire to learn new tools to increase your productivity. -Strong ability to learn as you go.-Ability to manage multiple projects and priorities in a fast-paced environment.-Analytical mindset with strong experience using data to drive decision-making.-Hands-on experience with marketing tools and analytics platforms.-Excellent communication skills and the ability to work cross-functionally.-Passionate about user experience, data-driven decision making, and continual learning.-Hardworking, thoughtful, and intellectually curious—a self-starter who values continuous improvement and collaboration.Why Join Us?-Be a part of an exciting growth journey at a dynamic company that is in growth mode ourselves.-Work with a talented, passionate, and collaborative team.-Enjoy opportunities for professional growth, creativity, and autonomy.To apply, fill out the form below. About the roleWe're going to offer a strong starting salary with a monthly bonus plan. Benefits are included.Base Compensation: to USD or local currency equivalent based on experience in this type of role. Bonus: Profit share to be reviewed as part of job offer. We're profitable and growing, and profit share potential is high.Potential for equity in the company will be considered after an initial evaluation period. #growth #minded #director #lifecycle #marketing