James Taylor, CEO, Decision Management Solutions

James Taylor, CEO, Decision Management Solutions

Viewpoints

Perspective

A Beautiful, Cloudy Forecast

Research by James Taylor, CEO of Decision Management Solutions, shows business potential in cloud-based predictive analytics.

Both “predictive analytics” and “cloud” are hot topics in business today. But what is the potential for predictive analyt­ics in the cloud? Research by Decision Management Solutions and a recent survey of more than 200 professionals hosted by SmartData Collective shows that cloud-based predictive analytics is poised for rapid growth.

Separately, predictive analytics and cloud solutions are changing the way organiza­tions do business. Together, they open up a wealth of opportunities. The use of predictive analytics in the cloud to improve focus on customers is particularly powerful. As early adopters look to build a sustained competitive advantage, organizations should have a plan to rapidly adopt this new approach to avoid being left behind.

Transformative Impact

Many survey respondents (43%) have already seen an impact from successful predictive analytic implementations. For an approach that has only recently become a mainstream topic in organizations, this is an impressive showing. Perhaps even more impressive is that 11% of respondents say this impact has been transformative.

Clear Results for Cloud Users

Early adopters of cloud-based predictive analytics are reporting impressive results. Here are some examples:

  • A fitness company that sells directly to consumers created a cloud-based analytic solution to build buyer and inquiry models to target catalogs for one of its brands. The results: For one campaign, the buyer model had a 25% higher response rate and 44% higher return on investment (ROI) compared with prior campaigns. The inquirer model had an ROI increase of 174%.
  • One credit card company, with the help of a big data analytics enterprise, used the solution to target bust-out fraud—where individuals suddenly max out their credit lines and then disappear. The analytics company was able to find and shut down fraud three to five days earlier than existing industry models, saving the card issuer more than $40 million per year in losses.
  • A clothing retailer that operates boutiques as well as online and catalog channels uses the new approach to differentiate between customer segments and market to them appropriately. It sends customers who buy items as soon as they are released a full-size, full-price catalog and mailings highlighting new merchandise. Discount shoppers get slimmer liquidation catalogs and sale fliers. Online customers receive emails geared to their buying habits.
  • One mobile phone operator used cloud-based predictive analytic models to run a set of pilot campaigns targeting both prepaid and postpaid custom­ers. While historical campaign response rates were around 1.5%, average response rates increased to 9.65%—a 643% spike. Two campaigns broke their response rate records, reaching almost 16% and 21%.

According to the survey, the top two areas for cloud-based predictive analytics were marketing/customer acquisition and customer retention. Other areas that scored well included the broader category of customer management, sales and cross-sell/up-sell. Fraud detection scored high for projects currently implemented. It is clear that the sweet spots for predictive analyt­ics in the cloud are effective acquisition, management and retention of customers. Cloud-based options are also very appeal­ing for geographically dispersed staff to work on customer-facing processes.

Addressing Concerns

While there is a clear optimism about these solutions, some issues remain. Data security and privacy were the top concerns in the sur­vey, with 65% listing them as very important. Regulatory issues, bandwidth for moving to the cloud and increased complexity were also identified as concerns. Each can be mitigated:

  • Privacy and security. Keep personally identifiable data out of modeling and leverage private clouds when necessary.
  • Regulatory issues. Work with vendors experienced in your industry.
  • Moving data to the cloud. Move data to the cloud directly rather than aggregat­ing first and then moving to the cloud.
  • Complexity. Work with experienced vendors and leverage more public cloud infrastructure.

Early Adopters Benefit

However, when a new technology is adopted, organizations must choose between being leaders or laggards. With cloud-based predictive analytic solutions, this balance seems to come down firmly on the side of being an early adopter. The gap between companies that implement these solutions now and their competitors is likely to widen as early adopters realize more posi­tive results, have fewer objections and more aggressively adopt the technologies.

The basic value proposition of predictive analytics in the cloud is clear: Organizations can make predictive analytics more scalable, more pervasive and easier to deploy. For many of the challenges organizations face in their journey to becoming analytic organizations, cloud-based solutions have much to offer.


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