Is product adoption the hidden growth lever your business is ignoring?
These days, in the boardrooms of most companies, the primary discussion is about growth. The drivers of growth tend to focus on ways to acquire new customers, i.e., build new products, enter new geographies, take share from the competition and create new segments. Yet there is a hidden growth lever many enterprises ignore —their current customers. By helping their current customers better use what they already have, businesses have the power to unlock significant growth.
Consider a simple test. Look around you for some common products you use, perhaps a television, a refrigerator or an automobile. Also think about some services you frequently consume like power, heating or even medical services. Now try recalling the level of continuing engagement you received from the providers of these products and services after you purchased the product or consumed the service. (Getting a bill does not count.)
Maybe, like me, you have noticed that the level of engagement from providers drops dramatically after purchase. I remember struggling with unclear instructions to mount my television to the wall fixture, and I am still unhappy with the behavior of the software, but no one from the television manufacturer has ever asked me for my feedback on the experience. My power company isn’t regularly engaging with me on my patterns of consumption or offering me insights on usage. I do not recall my medical provider reaching out to check in on how I am doing. Especially with the pandemic, shouldn’t that be expected?
You probably get my point by now. For high-value or high-frequency purchases, most customers expect some form of proactive engagement after purchase. They do not want more selling, but they do expect to maximize the return on the investment they just made. Yet few businesses deliver consistently on that expectation. Every business claims to be customer-centric but customer-centricity often ends at the point of purchase. The post-purchase experience often feels like an afterthought.
(Image Credit: https://headrush.typepad.com/creating_passionate_users/2006/08/why_marketing_s.html)
As I work with product and service teams, I have come to believe that a core element of every company’s digital transformation needs to be about finding new ways to engage with existing customers. Getting to know how customers use the products and services they buy from us can unlock new insights and reinvigorate our relationship with them. Helping them adopt and get value from their current purchases can lead to new opportunities to simplify product use and discover latent needs that lead to the creation of new products and services. Companies that have figured this out generate more than 30% of their annual sales from existing customers in the form of repeat purchases, up-sell and cross-sell. This is clearly a win-win, and there is real value for both customers and businesses in engaging this way.
So how can enterprises do this? There are three critical steps:
Reimagine the early adoption and product-use experience
Build a customer success organization to help customers achieve outcomes
Invest in design and data science teams focused on post-purchase
Reimagine the early adoption and product-use experience
I am often surprised by how few teams explicitly design the post-purchase experience. I find a good way to get these teams started is to focus on early product adoption. Most teams intuitively get the importance of this. Creating great adoption experiences starts from a foundation of deep empathy for users.
Learning or using something new naturally evokes feelings of anxiety in most of us, and the goal of the product-adoption experience is to counter this natural anxiety through well-designed (and even fun!) guidance and help. As an example, in one of our products at IBM, we noticed that churn rates had a significant correlation to the “first-hour” experience of the product. When users were unsuccessful in their first attempt at setting up a product, they rarely returned for a second attempt. We only had one shot to make the user successful. The team quickly designed a guided onboarding flow that provided step-by-step guidance and allowed users to access help when needed. We encouraged users to share their feedback on the setup process. In just a few iterations, we created an easy-to-use onboarding flow that resulted in a 3X increase in adoption and cut our churn rates in half. Lowering rate of churn early had dramatic effects on long-term churn rates.
Once early adoption experiences are in place, teams should use design thinking to actively reimagine the rest of the post-purchase engagement. In his book, Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation, Tim Brown talks about finding insights on usage by heading to the “…edges of the bell curve, the places where we expect to find extreme users who live differently, think differently and consume the product differently.” Tim describes how the Swiss company Zyliss designed a new line of kitchen tools by studying both children and professional chefs, neither of whom were the intended market for the products. By studying these extreme users, the company discovered valuable insights that helped it redesign the product to be much more usable by the target audience.
“Going to the edges” often means finding the novices and the power users and understanding the way they consume the product. The novices can challenge every assumption we make on product usability, and the power users help us design for extreme, high-frequency use. Meeting the diametrically opposite demands of these two distinct user cohorts can help product teams come up with exceptional post-purchase experiences that work across a broad spectrum of use cases.
Build a customer success organization to help customers achieve outcomes
As enterprises start to redesign their post-purchase experiences, they need to ensure that they are delivering the outcomes their customers had in mind when buying the product. Simply adopting the product and using it regularly does not guarantee customer outcomes. So what is the answer?
Software-as-a-service (SaaS) companies have long discovered a go-to-market construct that works proactively to get customers to reach their goals in a timely manner. That construct is Customer Success. Investing in a customer success organization is great for customers and for businesses. Companies with a dedicated customer success team see higher levels of NPS, a 15%-27% decrease in aggregate gross churn, a 50%-125% boost in expansion and at least a 10% boost in net dollar retention. As Nick Mehta, CEO of Gainsight, puts it in his book, The Customer Success Economy, companies that invest in customer success move from defense to offense.
Today, customer success teams collaborate with product teams to drive product adoption. The best customer success teams play a critical role in improving product-market fit. They help product teams prioritize the roadmap based on actual customer usage patterns. They engage with marketing to determine effective ways of driving customer expansion and retention. They help sales with references and efficiently matching customer segments with the right value propositions. Finally, they bring customer knowledge and context to technical support teams to drive faster issue resolution.
In a matter of a few years, customer success has gone from being a “must-have” in just SaaS enterprises to being adopted more broadly by enterprises in Industrial, Healthcare and other sectors. Enterprises are starting to see the value that empowered customer success teams can drive. Furthermore, as subscription models gain in popularity and digital engagements becomes a common part of consuming physical products (think Peleton app + Peleton bike), the need to have a customer success organization is becoming critical.
Invest in design and data science teams focused on post-purchase
Perhaps the biggest area of opportunity for most businesses to improve product adoption and the post-purchase experience is product analytics — the ability to understand product usage (and non-usage) through data. Being a data-driven business is a core part of digital transformation. Collecting, analyzing and acting on usage data is the weakest spot for most enterprises. A recent Harvard Business Review survey found that product analytics topped the list of analytics most companies believed had the biggest impact on customer experience — yet building the data infrastructure to do this and creating a culture of data-driven product management continue to be a significant challenge for most.
Over the past five years, a core part of my responsibility in IBM has been to build a supporting infrastructure and a data-driven culture within product management. IBM, like most enterprises, has long invested in using marketing analytics to drive efficient acquisition. We have the best data systems and understand with great granularity the behavior of users on our websites and the return on investment on specific marketing assets. But as I discovered, when I first took on the my role, we had very little understanding of user behaviors within products. It was as if our understanding of users stopped where marketing ended and never extended into the post-purchase phase of the user journey.
The problems I describe are not unique to any company. Enterprises of all sizes have historically underinvested in product analytics. At IBM, we prioritized three areas of investment to bring the same analytic rigor to post-purchase. This can serve as a blueprint for other enterprises trying to accomplish the same thing.
First, enterprises need to invest in a data and telemetry infrastructure that can capture and process user behavior data and aggregate it for analysis. The infrastructure needs to be built less with a reporting mindset but more with a user engagement mindset. This means the data infrastructure is less about dashboards for executive reporting and more about enabling real-time interaction with users through AI/ML. I’ll discuss more about this in a future article.
Second, enterprises should build separate data-science teams that exclusively focus on analyzing user behaviors within products. These teams should provide key insights to product and go-to-market teams on the customer experiences in-product. At IBM, we call these teams “growth teams.”. The key is to focus these teams on understanding friction points in early product adoption, continuing product usage, expansion and renewals. These teams should be responsible for predicting future behaviors and identifying the experiences that turn users into high-value customers.
Third — and this is probably the most crucial element — is to invest in product design teams that can translate data-driven insights into amazing user experiences. At IBM, we sub-divided the post-purchase journey into twenty granular use-cases (such as first login, product onboarding, upgrade, renewals, etc.) and created squads comprised of designers, “growth hackers” and product managers to improve specific elements of the post-purchase experience.
We now have a much better understanding of the post-purchase journey, and we are starting to see outcomes from this. Rich behavioral data allows us to create highly engaged customers in our products. Our data-science teams working with product designers are now able to apply AI and Machine Learning to personalize user experiences in real time. We have been successful in creating a data-driven feedback loop into product development, so product roadmaps become less about intuition and guessing. The culture shift is starting to materialize, but like most things about digital transformation, it is an iterative process.
Summary
Driving growth from product adoption and usage needs to become a big part of the growth story in every enterprise. It requires rethinking product and service design and accompanying go-to-market. This cannot be left to chance and requires explicitly and intentionally mapping the desired post-purchase journey. It requires building a customer success function that has the mission to ensure the product delivers the desired outcomes. Finally, designing the experience is an iterative process that requires building the underlying data and product telemetry infrastructure that is supported by dedicated data science and design teams.
Disclaimer: The views and opinions expressed are those of the author(s) and do not necessarily reflect the official policy or position of present or past employers.
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