Over a long enough horizon, most companies care about one single metric: profit. A strategy should be aimed at ensuring this long term goal over a sequence of decisions. These decisions need to be aimed at maximizing the profit over the short and may as well be hurting it in the short term.
Take the example of an e-commerce company trying to increase its’ revenue. It spends a considerable amount of time to set up an abandoned cart email journey. Through that process, they discover that only a small amount of emails end up being sent through their journeys. The reason is that only very few of the website visitors had a tied email address. The cause of that? Website visitors are not incentivized to log in before checking out. There are no nudges, no specific program to offer special discounts to login customers, the website allows for guest checkout, etc.…
They are deciding if pushing customers to login is a strategic move that needs to be considered. We need to weight the pro and the cons of such a move. Data cannot tell you if this move should be made, but it can inform the direction, by giving estimates on the importance and impact of each of the components of the decision.
Let’s take a look at some of these tradeoffs up the acquisition funnel, and let’s consider what happens in the case of a new customer that could be exposed to a guest checkout.
The new customer would be faced with one extra step in its conversion funnel. Is the value of forcing registration during that step of the funnel worth the potential decrease in the instantaneous conversion rate? Understanding the downstream impact of your decisions is important to make the right judgment call. These data points are not always readily available, and rough first orders estimates might be needed to drive the business forward.
What if, for instance, the website started offering a referral program, how would that be taken into account in those numbers?
If instead we looked at customers the hat have already registered, we can see benefits from forcing customers to create an account. Having the information available from a customer allows us to drive a significant amount of features, potentially increasing its lifetime value.
For instance, having a user’s details available allows to make the checkout process smoother. By login in, we can skip a few of the information filling that tends to cause a significant drop off in the conversion funnel.
Leveraging further a customer’s identity by having his/her banking detail information makes one-click ordering possible and allows for further simplification of the purchase funnel.
Besides its impact on the conversion flow, obtaining and driving a form of identity on one’s platform affects the accuracy and completeness of any data collected. As a result, customer segmentation improves, multi-sessions and multi-devices behavioral analysis become possible, personalization becomes more holistic, and we can more effectively retarget case of abandoned baskets, or lurking product views.
Given the incredibly diverse nature of the benefits coming from obtaining a better sense of identity, it is difficult to calculate the total benefits that could come from it.
How can one separate and evaluate what should be the focus and separate these tradeoffs when there are so many unknowns. Getting a better understanding of the business model and how it should fit the product strategy should help guide the strategy going forward.
Setting up business cases with estimates of what could be possible in terms of uplift based on certain assumptions over the long term helps drive these kinds of decisions. A company with highly frequent reordering rate would be more likely to favor a better sense of identity than a company more focused on a unique transactional relationship.
Looking at the potential benefit gained in terms of CLV compared to the potential negative impact on acquisition can help better inform this strategy by comparing some of their tradeoffs.
Finding the right mix of nudges and rules related to the login and registration flows as well as setting up the guidelines of identity on a website or platform, is what leads to impact. Ultimately the way this gets implemented and a focus on measuring minimal product increments that would allow us to get a better sense of the tradeoffs that are encountered and lead the final decisions on how the product should evolve.
There are ways to attempt to mitigate some of the negative impacts of these tradeoffs by rethinking how a product should be built. It might make sense, for instance, to ask a user to register on receiving their first shipment confirmation email, for example, or only on their second orders being created.
What should be your metrics of success in such an operation when you have to make tradeoffs scenario between registrations and initial conversions?
Little things such as the Amazon Sign-on nudge, as displayed above, can have an impact on the overall success of your product without having too many other downsides. It is the identification of these features, coupled with a longer-term strategy around data that can help more effectively achieve your goal.
Being data-driven in terms of product strategy means that we use data to better understand the different tradeoffs our product is facing. It also means providing the right sense of product direction. It is important to define one’s priorities, plan, measure, analyze, and continuously re-evaluate if, with new information, our sense of product direction is still on track. Only by incorporating all these components can a product strategy truly be data-driven.