The Power of Predictive Analytics
The business world has long known that solid customer relationships are key to profits and longevity. But as businesses grow, they find that their customer management systems don’t scale well. Customer relationships become less personal as companies depend less on direct customer contact and more on ineffective email blasts. And consumer segmentation by location, demographics, and other broad metrics may prove to be inefficient in highly-competitive markets.
To solve this problem, larger companies are turning to predictive analytics, the crown jewel of big data analytics.
Predictive analytics (PA), with its complex data modeling and parallel processing, does some things that earlier customer marketing systems could not do as well. It scales effectively. It also personalizes marketing data even for millions of customers at a time.
PA, as its name implies, also forecasts events, actions, or behaviors and suggests individualized ways to engage customers as a result of those predictions.
Companies use techniques such as targeted promotions, discounts, and differentiated pricing to elicit customer purchases. These techniques also generate very valuable data on what kind of outreaches trigger particular customer reactions, or no reaction.
PA also acts as a filter to fine-tune and reduce the cost of marketing campaigns. By filtering out high-risk prospects and reducing churn among good customers, companies increase the efficiency and lower the costs of their marketing campaigns.
More accurate demand-prediction can also benefit inventory. It’s expensive to keep inventory on the shelves, and running out of stock can be worse. Both negatively impact revenue and the latter directly impacts customer loyalty.
Cost of PA
A recent survey from Forrester Research found that 64 percent of B2B marketers are implementing or upgrading PA solutions, or they plan to do so within 12 months. But PA can be costly, even though costs are coming down.
Not that long ago, building your own PA system was extremely expensive. That kept many mid-sized and small companies out of the market. But in the last few years the cost of PA tools such as storage and processing power, along with software and expertise have come down.
The emergence of inexpensive cloud storage has lowered those costs. PA now runs on desktop computers operating in parallel so processing costs have come down.
A Human Element
There is a human element to the analytics process, particularly in the early stages–before solid mature models have been developed. Predictive analytics crunches historical and even current data and produces business insights. The quality of the insights reflect the quality of the data and the data models. And solid data models have a shelf life. They must be changed as market conditions change.
Building solid PA models and algorithms are complex undertakings, and even in the right hands they may require a lot of trial and error before they generate any real value. Even when the models seem to work they may still produce questionable results. Customers are human beings and humans can be…well…unpredictable. Even when the machines make what seems like good predictions, they may prove to be wrong in the real world.
Some customer qualities cannot be measured by data analytics. Qualities such as diligence or loyalty may not translate well into large batches of data.
For some of those metrics, companies may have to rely on their human marketers. In fact in the early stages of implementation the results should be validated by humans who know the business and the customers well.
Wi-Fi is also becoming an increasingly important part of the PA puzzle, as we’ve written many times here on boundless.