In recent articles, we reviewed why lifetime value (LTV) should be your North Star and relevant levers and benchmarks used to measure the impact of adjusting your go-to-market offers. The case studies below summarize how publishers have achieved measurable gains in subscriptions and revenue by leveraging the offer optimization framework and A/B testing technology.
These three case studies show how publishers are driving revenue and growth through applied analytics. Offer optimization can be applied to set a five-year strategy (case study No. 1), target low-propensity readers with compelling offers (case study No. 2), and confidently raise prices, knowing you are focused on long-term LTV (case study No. 3).
The case studies range from “big room planning” to micro-targeting users to renewal communication variants. But the common thread that binds them together is that LTV is the North Star, and the offer optimization framework is core to the success of these publishers.
Sometimes a complete pricing restructuring is required as offers get stale and subscriptions plateau. The offer optimization framework can give strategic planners an easy-to-understand comparison and what-if scenario modeling based on historical test data.
The publisher had conducted dozens of tests throughout the year and found insights on churn and start volume, but was still unsure of the best go-to-market offer, especially with revenue as the North Star metric.
Test data from the publisher was plugged into the offer optimization tool and calibrated with constraints of the billing system and market parameters. Multiple scenarios were simulated along with Mather’s recommended revenue-maximizing scenario. Due to the robustness of the prior testing, a high degree of confidence was estimated by the modeling.
Scenario modeling showed the revenue-maximizing approach generates 1.4 times the revenue with only a 6% lower subscription volume compared to the “volume at all costs” scenario. As expected, churn is 1.3 times greater in the revenue-maximizing scenario though LTV offsets the relatively higher churn. A low introductory price, long introductory term and high renewal were core components of the optimized go-to-market offer.
The publisher felt confident lowering the introductory price and extending the introductory term, and it was able to align key stakeholders with a focus on long-term value. The publisher is now implementing and testing new pricing to achieve long-term revenue targets.
Any marketer can tell you that lower offer prices will boost monthly starts. However, even with the offer optimization framework, publishers should be cautious about deploying discounts until they have exhausted other tactics such as tighter paywalls, registration walls and the introduction of premium content. Thus, the offer optimization framework can be applied to the most elusive customer segment: low-propensity readers.
The publisher noticed a significant number of users bucketed into Mather’s low-propensity segments and wanted to find a way to boost conversion rates. These users consisted of three sub-groups:
For each of these subgroups, the current go-to-market offer was stale and required a revamp.
The offer optimization framework was applied and a test was developed to offer a US$0.99 introductory price for six months for the low-propensity users. Each sub-group was split into two test variants. Half of each group was offered a US$4.99/month price point at renewal while the other half was offered US$8.99/month.
A summary table below shows the relative lift in net LTV of the US$8.99 offer vs. the US$4.99 offer.
Conversion rates were identical between the groups, validating the offer optimization framework methodology that consumers react to the introductory offer at purchase, not the listed renewal price. Churn in the US$8.99 groups increased to nearly 17% in the renewal month compared to just under 5% for the US$4.99 offer. Offer optimization works even for low-propensity users, boosting LTV by 1.3-1.6 times.
Further testing with the publisher focuses on identifying how content read at conversion (sports, news, etc.) impacts long-term retention and LTV.
Not only do users respond better to low-priced introductory offers, but churn is mitigated when the price is communicated in a lower denomination.
The publisher had accumulated a sizable number of digital-only subscribers paying the full price at the time of US$3.99 per week. As the “dollar-a-day” price gathered traction, the company wanted to test if this would work in its market.
Mather developed price test groups with renewal at US$4.99, US$5.99 and US$6.93 per week as well as a US$3.99 control group. Subscribers were stratified across tenure, engagement and other factors to ensure “apples to apples” splits. The publisher further split each group 50/50 to test communication variants on how the price was communicated.
This is an excerpt of the key paragraph (slightly redacted for anonymity): “…From time to time, we determine the need to raise subscription rates in order to continue producing the quality independent journalism you expect. Beginning at your next renewal, you’ll notice that your subscription rate … ”
The e-mail notice not only tested the different price levels but how the price increase was communicated: either the total billing amount or per-week rate (per day for the US$6.93 price). This summary table shows the variants in communication (A vs. B) and the range of renewal prices offered at renewal vs. the baseline US$3.99 control group.
In all cases, the higher price increase led to greater LTV, even with slightly higher churn. In all but one case, the churn was relatively lower when communicating the lower amount (variant A) in the notice. The US$0.99 per-day communication generated the most revenue and relatively low churn.
Mather continues to work with the publisher to expand the testing and further personalize the pricing by digital engagement and price elasticity.
These case studies demonstrate the level of sophistication and culture of testing being adopted by news media organizations. The testing also validates the framework and methodology developed by Mather’s data scientists and economists as well as predictive power to find the right offer for each reader.
If you are regularly pivoting strategy between bursts of focus throughout the year on volume vs. churn vs. ARPU, stop the cycle and reach out to our team to optimize your offers!
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