![\"Olabinjo](\"https:\/\/static.wingify.com\/gcp\/uploads\/sites\/3\/2025\/04\/Feature-image-Too-Much-Personalization-Overwhelms-Users-and-Complicates-Their-Experience.jpg\")
<\/figure>\n<\/div>\n\n\nLeader: <\/strong>Olabinjo Adeniran<\/p>\n\n\n\n Role: <\/strong>Senior Lifecycle Marketer\u00a0<\/p>\n\n\n\n Location: <\/strong>London, United Kingdom<\/p>\n\n\n\n Speaks about: <\/strong>Growth marketing \u2022 Product marketing \u2022 Performance marketing \u2022 User acquisition and retention strategy <\/p>\n\n\n\n Olabinjo Adeniran is a global technology professional with over a decade of experience leading growth and product marketing for companies across fintech, travel, media, eCommerce, and crypto. He\u2019s not only shaped strategies for some of the fastest-moving tech industries but also authored two books (The Growth Handbook – Scaling Fintech in Nigeria<\/a> and 30 Days of Growth<\/a>) and runs Growth Case Studies<\/a><\/em>, a monthly newsletter exploring how tech companies scale.<\/p>\n\n\n\n This interview is a must-read if you’re looking to bridge the gap between user experience and business growth. Olabinjo\u2019s approach centers on deeply understanding the user and elevating their product journey in ways that not only meet expectations\u2014but drive real business results. His perspective offers a clear, actionable path to building products that people love and that perform.<\/p>\n\n\n\n The internet, data, and software have birthed unique marketing practices for the software business beyond customer acquisition. Growth marketing is the business term that encapsulates these marketing functions for software and digital businesses.<\/p>\n\n\n\n Lifecycle marketing is a subset of growth marketing. It involves managing owned channels and optimizing bottom-of-funnel activities (specifically owned channels) toward key business results. Owned channels here are SMS, notifications, in-app screens, and email.<\/p>\n\n\n\n It is pretty much a growth marketing function and relies on growth marketing practices such as conversion rate optimization, customer journey optimization, personalization, a hypothesis-driven approach, and user experience optimization.<\/p>\n\n\n Data, attribution, and prioritization are the biggest challenges.<\/p>\n\n\n\n Data<\/strong> \u2013 A product that’s fast-growing or has scaled faces data challenges. We grapple with what data software to use, how they work, how to deploy them properly with our product and tech stack, how to manage costs, and how to integrate them with different tools. Some data warehousing software tends to overpromise and underdeliver. In addition, the only method to know is to test\u2014this ends up adding more cost and time in our efforts to properly measure data.<\/p>\n\n\n\n Attribution<\/strong> \u2013 As a company scales, it becomes more difficult to attribute certain success or product events to one-click messaging and specific channels, especially when there are multiple campaigns running toward different in-product goals and businesses.<\/p>\n\n\n\n Prioritization<\/strong> \u2013 It even becomes more challenging to prioritize messaging as a company ramps up products or builds more features, and all or some of these features require customer adoption. Several business cases come up in different jurisdictions, sub-organizations, and product teams. It’s tough to prioritize efforts toward a specific set of goals. The best lifecycle managers are heavy on sticking to priorities and unfortunately have to lose a few friends in the journey because you can’t please everyone.<\/p>\n\n\n\n A\/B and multivariate tests are a must for anyone working in lifecycle marketing. I typically build hypotheses at a specific level and work from there.<\/p>\n\n\n\n E.g., let\u2019s say we\u2019re a fintech app that\u2019s started a new acquisition campaign\u2014we see large sign-ups, but 70% of users don\u2019t finish the KYC process. The lifecycle team is tasked with building multiple hypotheses to test why KYC isn\u2019t completed, testing out those hypotheses in specific user segments or control groups, and then coming up with a conclusion and solution that either scales a new type\/format\/channel of messaging or not. We pretty much follow the scientific method: Observation > Research > Hypothesis > Experimentation > Data Analysis > Conclusion.<\/p>\n\n\n\n I also collaborate closely with product, data, and UX research teams for some of my experiments, especially when I’ve validated a hypothesis and need to scale it across the product.<\/p>\n\n\n\n Testing is the bedrock of a solid lifecycle marketing professional. I always assume that I don’t know\u2014even when I think I know. <\/em><\/p>\n<\/blockquote>\n\n\n I analyze the full funnel using a mixture of tools\u2014all connected to each other and presented via a dashboard. First, we set up events in the product, tag those events in our tools, and build a step-by-step visual to represent what the funnel looks like. For example, at MetaMask Portfolio, the funnel for a swap looks like this:<\/p>\n\n\n\n User visits page \u2192 User connects wallet \u2192 Clicks Swap \u2192 Selects Blockchain \u2192 Selects Token to swap from \u2192 Enters Value \u2192 Selects Token to Swap to \u2192 Clicks Get Quotes \u2192 Confirms Swap.<\/em><\/p>\n\n\n\n I create various funnel visualizations based on the sequence of events for the product feature or business goal I am optimizing. We don\u2019t necessarily want to capture every step in the flow, but the most important steps from visitor to actual paying customer.<\/p>\n\n\n\n I use tools like Segment, Mixpanel, HubSpot, Customer.io, Google Analytics, and Looker. I also work with a data analyst to ensure I’m capturing the appropriate data points, measuring the right metrics, and attributing them appropriately.<\/p>\n\n\n\n Understand how users behave with VWO Funnels\u2014map their journeys, uncover friction, and identify why they drop off. Apply segmentation like device type, query parameters, and custom events to sharpen your insights. Capture and annotate key findings in real time to drive smarter, faster optimization decisions.<\/p><\/div><\/div><\/div><\/div>\n\n\n\n I work closely with product marketing, customer success, and user experience research. These three groups have closer relationships with the customer than I have. In my most recent role, I relied on the product marketing team for deeper insights into the brand and customer psyche, the customer success manager for common complaints and pain points, and the user experience research team for new methods of deploying messaging.<\/p>\n\n\n\n The way to balance is to talk to customers and get constant feedback while looking at the quantitative data. There’s a danger of leaning too much to one aspect and ignoring the other. In my experience, one method doesn\u2019t trump the other.<\/p>\n\n\n\n E.g., if we launch a new product feature, we could gauge customer success by how many users adopt the feature within a specific period. This is also an opportunity to actually speak to customers and gather feedback that helps us better market the feature and position it to the appropriate audience segment.<\/p>\n\n\n\n A dashboard will often show us whether our new feature is a hit with customers or not. However, a dashboard doesn’t show us how customers feel about the feature or what questions they might have.<\/em><\/p>\n<\/blockquote>\n\n\n\n Personalization is the most critical aspect of lifecycle. Our goal is to solve the user\u2019s problem as it presents itself to suit the user\u2019s exact needs. There are multiple ways to personalize a product. We could optimize the product itself for different use cases or user buckets. <\/p>\n\n\n\n We could select points in the user journey to build educational messaging that solves the user\u2019s problems. Or we could promote certain features based on the user\u2019s specific journey. We could also do these things by location, language, demographic, and in-app habits.<\/p>\n\n\n\n There\u2019s a danger in over-segmenting customer data to the finest and minute event points. It leads to over-messaging, and the product or brand becomes burdensome from a customer point of view.<\/p>\n\n\n\n For example, do you really need to send an email at every point in the customer journey? Is it better to show personalized messaging in the app instead? Yes, we need to send the right product message to the right customer at the right time, but we need to do this in an efficient and cost-effective manner\u2014we simply can’t send deals or discounts every time any person checks out a new feature.<\/p>\n\n\n\n The big data trend has gotten many companies to over-segment data. It’s led to over-personalization that has eaten into company margins.<\/em><\/p>\n<\/blockquote>\n\n\n There are multiple different reasons.<\/p>\n\n\n\n One is that some companies make enough revenue and see healthy margins, so they don’t see the necessity for a fully integrated lifecycle strategy.<\/p>\n\n\n\n Another is a lack of awareness about the tools they could use to achieve customer success. I was talking to a friend just yesterday about this. Despite all the technology and improvements with cart abandonment for e-commerce and digital services, there are a lot of companies who never follow up when a customer abandons a cart. Whether this is retargeting ads, emails, or notifications\u2014many companies do nothing because they don’t know how.<\/p>\n\n\n\n The third is outdated software. Some web and mobile services run on outdated software that cannot pull and push information to modern web services. It’s actually a technically difficult problem to solve, so an engineering team might find it overwhelming because it requires overhauling legacy systems.<\/p>\n\n\n\n The fourth is expense and capacity. At a certain level of customer scale, businesses are large, and they cannot afford the investments required for lifecycle marketing. It’s always best to build the lifecycle marketing practice as soon as a business hits product-market fit.<\/p>\n\n\n\n We\u2019re seeing AI used in quick experimentation and code deployment. In a recent role, my manager and I had some coding experience and were able to code a prototype in a few minutes, test it, and confirm how well it worked. While our stellar engineering team needed to review this prototype, it gave us the autonomy to build and show colleagues what our experimentation idea looked like in practice.<\/p>\n\n\n\n But even when provided with our content library, some of the AI tools consistently fail to create content that aligns with our brand and tone of voice. I’m not sure if this will get better soon, but I’m hopeful that tools like Spiral help us create better content faster.<\/p>\n\n\n\n I’m also hopeful that LLMs can better optimize targeting, messaging, and placements. I use a bunch of tools in my work (HubSpot and Customer.io), for example, but haven’t seen any customer-facing improvements in lifecycle since GPT was published. I’m hoping that LLMs can better help us understand disparate user data and make sense of them in a structured manner so that we can provide more value to customers.<\/p>\n\n\n\n For teams without engineering experience, AI has not significantly impacted lifecycle work beyond content generation and modification. I have tried using several different chatbots and content software to create content but found that they dilute the brand. <\/em><\/p>\n<\/blockquote>\n\n\nWhy should you read the interview? <\/strong><\/h2>\n\n\n\n
What really separates lifecycle and growth?<\/strong><\/h2>\n\n\n\n
![\"Growth](\"https:\/\/static.wingify.com\/gcp\/uploads\/sites\/3\/2025\/04\/Storytelling-1.jpg\")
<\/figure>\n<\/div>\n\n\nWhat makes lifecycle marketing difficult<\/strong><\/h2>\n\n\n\n
How testing improves the user lifecycle<\/strong><\/h2>\n\n\n\n
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![\"Steps](\"https:\/\/static.wingify.com\/gcp\/uploads\/sites\/3\/2025\/04\/Steps-for-testing-in-lifecycle-marketing.jpg\")
<\/figure>\n<\/div>\n\n\nSteps to effective funnel analysis<\/strong><\/h2>\n\n\n\n
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How customer research informs lifecycle decision-making<\/strong><\/h2>\n\n\n\n
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Perks and perils of over-segmenting your data<\/strong><\/h2>\n\n\n\n
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![\"Results](\"https:\/\/static.wingify.com\/gcp\/uploads\/sites\/3\/2025\/04\/Steps-for-testing-in-lifecycle-marketing-1.jpg\")
<\/figure>\n<\/div>\n\n\nRoadblocks in adopting lifecycle strategy<\/strong><\/h2>\n\n\n\n
Where AI wins and where it lags<\/strong><\/h2>\n\n\n\n
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![\"AI](\"https:\/\/static.wingify.com\/gcp\/uploads\/sites\/3\/2025\/04\/AI.jpg\")
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