3 ways you can use data science to drive renewable services revenue

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In the ongoing quest to drive renewable services revenue, channel organisations are increasingly turning to the power of data to unlock and deliver high-impact results. But to get the desired outcomes, data needs to be mined with the proper tools and approached in the proper way.

Data science employs statistics, historical data and business intelligence to build models that can provide a range of valuable information. Incorporating advanced mathematics, neural networks and predictive analytics, data science can predict how customers will respond under various business conditions, and the actions they will take specifically to a firm’s products and services.

Armed with such information, channel organisations can take actions that deliver the best chances for the desired outcome. For example, knowing the optimal time to offer service renewals can significantly increase the number of contracts renewed. Such intelligence can be incorporated into marketing and sales systems to automate the renewals process on a larger scale, leading to significant incremental renewal revenue opportunities. Of course, the more accurate the data, the more precise and beneficial the resulting models will be.

As you consider the use of data science to drive sales for your business, follow these three guidelines to gain an understanding of what a robust, channel-driven initiative might include:

1. Business Intelligence Basics: Business intelligence (BI) leverages historical data, and lends itself well to enabling visual dashboards and reporting current conditions and status updates. Using the service renewal example, BI analysis might offer visualization of upcoming expiring service contracts or perhaps products sold without service agreements attached. Such intelligence can be turned into immediate action with marketing campaigns or sales initiatives to drive service sales. Even further, BI combined with additional data science capabilities can provide rich forecasting of services buyer behavior and likelihoods, revealing upsell and cross-sell opportunities and enabling channel companies to make course corrections.

2. Predictive Modeling Primer: Predictive modeling mines internal or external industry-specific expertise, combines that with components of data mining, to identify correlations that might lead to insightful behavioral projections. In the renewal example, channel organizations can train their models to predict service renewal buying behavior or compute the most optimal upsell and cross-sell opportunities on the horizon. With increased use and ongoing campaign data and results, the model becomes increasingly accurate and powerful, leading to more personalized – and profitable -- service quote parameters.

3. Prescriptive Analytics Perspectives: This element of data science analyses various campaign offers and outcomes to determine the optimal offering that delivers the best results. For example, say a service renewal campaign incorporates the following four distinct $100 offers, associated discounts and outcomes:

- $0 discount → 30% probability of purchase

- $10 discount → 60% probability of purchase

- $20 discount → 80% probability of purchase

- $30 discount → 85% probability of purchase

Note that the last $10 discount increase to $30 yields only a 5% increase in the probability of purchase. By monitoring the effect of the discount on the customers’ probability to purchase, channel organisations can come up with the proverbial “sweet spot” that is the best discount to offer that will yield the optimal purchase and revenue results. Some resellers with different goals and priorities might view the results differently, and put more emphasis on the probability to buy. Others may focus on dollar value or profitability of each quote.

With advanced data science techniques, this simple example above can be significantly amplified to use a multitude of scenarios, including possibly hundreds of quotes to a single client, using different combinations of attributes, promotions, service levels, products and services, etc. Such values can then include the client’s probability to purchase and the associated revenue with each particular offer. The combinations and outcomes are then ranked to determine which quote to actually create and deliver to a particular client.

Lastly, key performance indicators (KPIs), reporting and tracking of results are also necessary for making changes and course corrections, and ensuring that marketing and sales programs are aligned and working in tandem to deliver the best results.

Most of all, remember that data science is not a far-off vision for the future. It’s already having an impact today, proving its worth to channel organisations as an investment to better understand customer preferences; respond with smarter, timelier offers; increase response rates; and generate incremental revenue. Combine all of that and you’ll find that it’s a clear winner for resellers.

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