There is a benefit to keeping strategies and tactics simple, often reflected in the saying “KISS — Keep it simple, Stupid.” In some circumstances the investment in complexity is justified by the return, particularly when complexity can be automated, and I propose a modification of the KISS acronym to “Keep It Simple, Sometimes.”
Complexity has a high return on investment (ROI) when it comes to personalization of customer experiences and relationships. Personalization of content recommendations, acquisition offers and retention campaigns supported by analytics that provide propensity scores for subscription likelihood, churn risk and healthy engagement levels are examples of when complexity is justified. Operationally, propensity scores are “pushed” to the applications in publisher’s tech stacks that execute these personalized tactics at the appropriate customer touchpoints. This practice is “mass personalization at scale.”
Implementation of mass personalization use cases is often more challenging than the underlying data science, although high-quality data science is very important for success. Personalization of customer touchpoints needs to work within the capabilities of the tech stack and reflect the strategic objectives of the organization. Incorporating A/B testing into the implementation of personalization to verify the tactics are creating lift and to optimize the messaging is a best practice. Strategic objectives can be reflected in business rules that guide the recommendations provided by the analytics.
Subscription pricing is an area where executives often seek simplicity, and there are cases where it is helpful. We have found the ROI on pricing complexity is very high, and we will use this post to describe where simplicity in pricing is helpful and where complexity can dramatically improve results.
Personalized pricing decisions
In a subscription relationship, there are three key pricing decisions during a customer’s lifecycle: acquisition offers, the transition to regular rate after the promotional offer and annual increases. For an individual customer, about 80% of pricing decisions are annual increases since you acquire them once and keep them for many years. [Note: we use the term annual increases, but this decision can also be called a renewal increase if you have subscription terms.] We find that the first two pricing decisions are the best for optimizing volume and the last one for optimizing revenue.
Acquisition offer design includes an introductory price point and an offer length. Industry trends are to have low-price offers that provide access for several months: $1 for six months is an example. These offers generate significant volume, but subscribers from these offers usually have high churn rates. We have tested hundreds of subscription offers with publishers, and we can share benchmarks for conversion rates, churn rates and lifetime value.
Simplicity works best with Acquisition offers among the pricing decisions. Offers that are easy to communicate, understand and remember perform well. Offers can be targeted to consumers by channel or for readers where you have data to support propensity models, but in most cases the ROI from targeted pricing at this point is not as great as later in the subscriber lifecycle.
Transitioning a subscriber from the acquisition offer to the regular rate is a challenging step in a subscription relationship. Customer engagement is an important predictor of retention through this phase, and many publishers are testing proactive retention offers for high churn risk customers reaching the end of their promotional offers.
Many countries require disclosure of the price the customer will pay after the promotion offer. We find that the “next rate” does not affect conversions at the point of purchase, but higher rates do cause higher churn upon transition. It is ok to move a customer to a lower rate than what was stated at acquisition. Targeting customers for lower transition offers is a challenging business case since the required retention lift to offset the lower ARPU is a high bar to reach, but complexity in the form of targeting does have a positive ROI.
Dynamic pricing of annual increases is where additional complexity has a significant ROI. Segmenting your customers by price elasticity can identify those subscribers most likely to stop after a price increase. The 80/20 rule, where 80% of your price stops are coming from 20% of your customers, is an accurate description of the opportunity for optimization. Avoiding or minimizing price increases to high price elasticity customer segments can significantly reduce churn and increase the net revenue yield from pricing changes. Giving higher increases to those customers that place a high value and who can afford higher prices offsets the lower yield for high-risk customers.
We find that digital subscribers have similar price elasticity to print customers, and increasing ARPU from digital subscribers is necessary for sustainable digital business models, as we wrote about in this blog recently.
How does our Data Protection Officer look at this?
Keep it Simple, Sometimes
Dynamic pricing can be implemented in a relatively simple pricing framework. A few price points are sufficient to realize most of the gains, and we can advise you on the tradeoff between more price points and higher yield.
We agree with the principle that simplicity is best in most cases; however, the increased sophistication of modeling approaches and the capabilities of technology tools provides opportunities for publishers to use complex strategies and tactics effectively.