A few weeks ago, Salesforce announced its new Artificial Intelligence (AI) functionality called Einstein. The new features promise to provide improved decision making based upon predictive scores and recommendations to sales, marketing, service, and other functions. Likewise, Microsoft announced yesterday that they have formed a dedicated AI group working on infusing Microsoft products with intelligent capabilities.
However, as AI and Predictive Analytics become key technologies for companies, it is important to remember the old GIGO maxim:
Garbage In, Garbage Out
These tools simply won’t work well if your information is inaccurate, out of date, or incomplete. Best case, bad data results in weak predictions that aren’t trusted. Worst case, they provide a false confidence that wastes resources and misdirects corporate activities.
John Bruno, an analyst at Forrester, described this problem well in a recent blog:
The future analytics-driven sales processes is bright, but the path ahead is not without its challenges. Current and potential Salesforce customers should be mindful that intelligent recommendations require a large volume of quality data. If poor data goes in, poor recommendations will come out. Cleansing data and iterating the fine-tuning of recommendations will be vital to long-term success. Another major hurdle is adoption. Many sellers still lack trust in “intelligent” recommendations. You will need to handhold these sellers until they form trust. This means starting with small recommendations and scaling from there.
The good news is that many of the sales intelligence companies are now offering data hygiene services for lead, contact, and account records. The processing can be performed via CRM or MAP connectors or by uploading files to their cloud services. The vendors match sales and marketing files against their reference datasets and then augment the files with firmographics, biographics, technographics, etc. Matching can be done both in real-time to support both list uploads and web forms and via batch processing to support on going maintenance of corporate data.
While no company and contact database is 100% accurate, they are far more accurate than most marketing automation platforms and CRMs. Furthermore, they have better field fill rates, standardized values (important for segmentation and analytics), and more rapid update cycles.
The predictive analytics companies are also beginning to provide enrichment services.