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Revelwood helps marketers hit the bullseye with cloud-based data science

Worldwide Client Reference Manager, Enterprise Data Science, IBM

As industries become more competitive, disruption is becoming part of everyday business. Marketing teams are playing an ever-more critical role in ensuring their organizations continue to gain customers’ attention and win their trust. 

However, marketing initiatives that target every prospect with the same messages are no longer affordable or effective. To maximize return on marketing investment, companies must take a much more scientific approach, executing tightly focused campaigns tailored to inspire each customer to take the next step on their purchasing journey. 

Machine learning has the potential to make the lives of marketers easier, but few marketing teams currently have the in-house data science skills they need to take advantage of it. 

That’s why Revelwood developed BULLSEYE, a cloud-based machine learning platform that includes dozens of predictive models and powerful visualization and reporting tools for marketers. By feeding in their sales and customer data, businesses can get fast, accurate answers to questions around customer retention, propensity to purchase, engagement, profitability and more. 

The results are impressive: one of Revelwood’s clients saw a full return on investment within 14 days and a 300 percent lift in revenues attributed to campaigns driven by BULLSEYE.

Making data science accessible

To build BULLSEYE, Revelwood needed a platform that would make it easier for a wide range of users — from developers and data scientists to statisticians, business analysts and line-of-business users — to collaborate. 

Traditional data science tool chains tend to be fragmented and complex to maintain, which raises a significant barrier to entry for less-technical users. For marketing teams to adopt BULLSEYE, a much more user-friendly environment was needed. 

That’s where IBM Watson Studio comes in. It brings together state-of-the-art open source data science tools with best-of-breed IBM solutions such as IBM SPSS Modeler. The whole environment runs in the IBM Cloud, making it simple for clients to manage and maintain, and easy for users to access from anywhere. This central platform engages all kinds of users and helps manage the data science workflow from end to end. 

Watson Studio also includes the data governance and security features that you would expect from an enterprise-class solution and which are so difficult to get right when you have to build them from scratch. In short, building on Watson Studio helped Revelwood avoid having to reinvent the wheel, thereby getting BULLSEYE to market much faster.

Getting marketing messages on target

Clients who are using BULLSEYE have already seen significant positive outcomes. For example, Revelwood’s solution helped one major US insurer build a 360-degree view of customer preferences across multiple touchpoints. This enabled the company’s agents to identify high-potential prospects for each product and reach out to them on their preferred channels. In trials, agents who used this customer analytics solution made twice as many sales as those who didn’t. 

The results we quoted earlier in this article — a full return on investment within 14 days and a 300 percent lift in campaign revenues — were achieved by another BULLSEYE client, working in the wholesale industry. This client used machine learning and predictive models to uncover sales trends across tens of thousands of SKUs. As a result, it was able to derive highly personalized product recommendations for its customers, which powered a highly successful set of marketing campaigns. 

That may be an extreme example; but, even so, in most cases clients typically see a return on investment in BULLSEYE and other IBM Watson Studio solutions in six to nine months. These stories are a testament to IBM and Revelwood’s ability to make data science accessible and harness the power of machine learning to drive next-generation marketing campaigns. 

To learn more, read the case study.