Posted by Srividya Sridharan on September 10, 2013
SAP today announced plans to acquire KXEN, a provider of predictive analytics technology. The terms of the deal are not known. This is an interesting development for both companies and highlights the focus on the democratization of predictive analytics, especially for marketers. The proposed deal puts the spotlight on two shifts in the analytics landscape:
- Expert user to casual user. Our research shows that finding top analytics talent is a key inhibitor to greater customer analytics adoption. As a result, users expect analytical tools to cater to nontechnical, nonstatistician business and marketing users.
What does this mean for both players?
o Target audience alignment: KXEN has experience in catering to this exact profile of users, by delivering automated predictive modeling solutions for clients. KXEN clients have seen significant productivity gains with this automated modeling approach that condenses data preparation time and makes more time available to analysts to “analyze” results. But for KXEN customers, this could present new avenues for exploring SAP’s existing analytics solutions or they run the risk of KXEN becoming too embedded in SAP’s analytics menu of solutions where KXEN becomes an “Intel Inside” story.
o Marketing ecosystem penetration: SAP strengthens its access to marketing and lines-of-business users of predictive analytics by leveraging KXEN’s focus on solving for marketing problems across the customer life cycle such as acquisition, retention, cross-sell/upsell, and product recommendations.
- Analytics production to analytics consumption and activation. The bare minimum expectation from any customer analytics technology or platform is the availability of a range of data-mining methods to solve for customer experience and marketing problems. For customer insights professionals solving marketing problems using advanced analytics, it is not so much about the breadth and depth of algorithms and analysis methods available through the tool, but about the ability of the platform to make analytics output consumable by business stakeholders through better visualization and tighter integration with marketing execution engines such as an email service provider or cross-channel campaign management technology.
What does this means for both players?
o Predictive analytics ups the game with advanced data visualization. Here is where SAP’s strong advanced data visualization heritage can provide a foundation to bring KXEN’s predictive expertise to the forefront. KXEN’s analytics consumption capabilities have focused on providing users with model performance snapshots and results of statistical and data-mining exercises. KXEN does not have native dashboard features and its data visualization capabilities are fairly basic. For more on implications on the BI landscape, see my colleague Boris Evelson’s take on the deal here.
o Activating insights at the point of customer interaction. For SAP this presents a larger opportunity to accelerate into marketing execution, where the output of analytics is actually executed by marketing technologies like recommender and personalization systems, campaign management tools or email marketing technologies.
The customer analytics market is abuzz with emerging and smaller players hoping to make analytics more agile, accessible and easy for marketing scientists to use. They are challenging the notion of enterprisewide, code-based, statistician-reliant solutions for customer analytics and provide lines of business with a quick way to get started with their analytics needs with minimal resources. Long-standing customer analytics players are already responding to this by introducing marketer-specific analytics products. But the democratization will come with a price in the short term where the power of analytical insights can result in low quality output. In the long term, customer analytics and customer insights professionals will benefit from the critical mass of analysts knowledgeable in how to create actionable and meaningful insights from customer analytics.
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