Operators should be forward-looking when it comes to analytics. Research firm Gartner cited a shift to such an approach by including “Next Generation Analytics” in its Top Ten Strategic Technologies for 2011.
Gartner explains that in 2011, analytics needs “to predict the future outcome, rather than to simply provide backward-looking data about past interactions.” Furthermore, companies need to leverage these predictions “in real-time to support each individual business action.”
There’s no doubt that predictive analytics can help service providers do a number of things: increase revenues, optimize processes, identify new opportunities, and anticipate problems before they happen.
When it comes to the mid-market segment, in particular, there are several key benefits. Whether it’s applying analytics for the purposes of cross-selling, customer acquisition, price optimization or churn reduction, arming sales teams with forward-looking customer insight can drive more efficient, timely, and intelligent action. The result is an enhanced customer experience and increased profitability.
While traditional business intelligence (BI)
provides companies important knowledge based on what has happened in the past, predictive analytics uncovers relationships and patterns within very large volumes of data that can be used to predict future behavior and events. Unlike other BI technologies, predictive analytics is forward-looking, using past events to anticipate the future.
Research commissioned by IBM reveals
four key purchasing criteria that drive mid-market customers to make buying decisions. These are return on investment, financial impact, time to value, and whether the partner’s solution maps to knowledge of the customer’s business.
Enterprise sales representatives are expected to know their customers – to understand their business model, be aware of their products and services and be able to anticipate future needs.
Applying the same focus to mid-market customers, however, becomes challenging as the customer-to-sales rep ratio is often much higher. In many cases, data overload makes being able to organize, analyze, assess, and act in a timely, intelligent manner a challenging task considering the numerous stakeholders, siloed data sources and a lack of efficient tools.
So how can a mid-market sales rep who has more than 100 -- and in many cases more than 1000 -- customers transition from a reactive order taker to a proactive sales consultant?
provides insight into future behavior and events. By applying statistical algorithms, predictive analytics identifies patterns that can be used to predict future actions.
These predictions, often expressed in the form of a ranking or score, can forecast customer behavior, market trends, the purchase rate of a specific product or service, the response rate to various discounts, the likelihood that a customer will make a repeat purchase, and virtually any other factor that can be statistically analyzed.
The reality is that whether a service provider is focused on the mid-market segment or not, it can apply predictive analytics to impact three key business objectives.
First is profitability or revenue generation. Pinpoint when a customer is most likely to buy a new offering, which customers are not likely to buy, and which customers will only buy if there is a promotional offer to ensure marketing spend and sales focus are channeled effectively.
Second is customer retention or price optimization. Analyze current product sets and trended behavior to anticipate future needs, avoid bandwidth constraints, protect against overage charges, proactively deliver the best value to customers, and determine the impact of changes in customer pricing models.
Third is loss reduction or risk management. Analyze data to optimize network performance and collections, minimize revenue leakage, and increase retention and loyalty of profitable customers.
Duane Edwards is co-founder and senior vice president for Globys