How have we increased the Brand24' ARPU by 23% by sealing margin leakages in discounted accounts?
Impact Case Studies
December 28, 2021
23% ARPU increase for Brand24
Client: Brand24 is a publicly-traded company offering real-time social media monitoring and analytics. It is designed to keep track of online conversations about a brand and its products. Brand24 collects real-time social data from millions of sources around the web. Their web-based dashboard provides actionable customer insights, email alerts, influencer analysis, automated & customized PDF reports, infographics, etc. The tool allows measuring critical metrics around buzz and sentiment.
Over 30,000 brands widely use its international version worldwide to monitor their PR & marketing efforts. The company serves a few most prominent customer logos globally, including H&M, IKEA, Intel, Carlsberg, Discovery, Vichy, and Leroy Merlin; however, their main client focus is around small and medium businesses that generate the majority of revenue.
Michał Sadowski, a Brand 24 CEO, is named a #1 Polish marketing influencer, and we have leveraged his outstanding social media reach during the pricing engagement (i.e., to gather survey responses).
Michał Sadowski, Brand24 CEO: "I was skeptical towards pricing research, but we know much more about our customers and the value we create as a product. Overall, the business impact of the pricing changes and ROI of the project was so high that we even had to communicate it to our investors. That means something."
Situation: Brand24's management hypothesized that the current pricing scheme neither captures nor signals the value to the distinct buyer personas, including marketing managers, PR companies and agencies, data analysts, and content creators. Initially, the product communicated three plans, differentiated primarily with the monitored keywords and mentions volume; some feature differences were minor for the overall picture considering total price differentiation.
Brand24 approached the Valueships team with a request to organize a value-based optimization initiative, including price increases and reengineering a current pricing model.
Goal: The main objective was to increase ARPU by considering rising software development costs. Another priority was creating a new pricing model that would effectively communicate the product value in different market segments.
Approach: We started with a granular revenue engine diagnostic. The analysis of the data delivered by the Brand24’s crew allowed Valueships analysts to discover that roughly 30% of the customer base paid less than the catalog price indicated. Such an outcome resulted from a relatively liberal discounting policy, which aimed to retain customers who planned to leave. The Valueships team calculated the impact of sealing this discounting leakage. We estimated that the impact of introducing the stricter policy in this area would account for a 10-15% MRR increase on discounted accounts.
Next, we created ROI models considering four different scenarios that could pan out after increasing the prices among heavily discounted customers. Each model had an additional risk parameter linked to the actual price increase applied to a given account. Our models suggested that the churn rate would need to increase to improbable levels (30%+) to make the whole discount policy sealing unprofitable.
Afterward, we run a thorough competitive scan to assess Brand24's market positioning. We have used the data points available via public sources and databases to benchmark the company against its peers. We have realized that Brand24 enjoys outstanding net promoter scores and provides unusual customer ROI payback vs. other tools. Their overall pricing was far below the market value line. In other words, the company charged too little for the value they made. Also, they have used an entirely different primary value metric (keywords) than the market consensus (scrapers or mentions). Regarding pricing strategy, the product was not directly comparable, and such a thing allowed for better, more differentiated positioning.
Last but not least, we survey research using advanced techniques designed for pricing. Besides identifying key price points, the survey helped us understand which product features were perceived as table stakes and differentiators. This was vital for re-modeling the pricing page and preparing new product packages that would be suited for the actual needs of different users.