Ensure You Have the Correct Data Source Before Considering Predictive Analytics
Right Data Inputs will Get You the Results You Desire
Data sourcing has become easier than ever in B2B tech marketing. However, marketers are still trying to find effective methods to leverage that data to achieve productive action. This is particularly important while building an effective ABM strategy. You need to have the right definitions in place and make sure that data is being used to its highest potential.
With the advent of predictive analysis solutions, there’s a hope that B2B marketers can leverage AI and machine learning to ensure effective registration and conversion of prospects. Though predictive analysis is vital for B2B marketing, it cannot give you crystal clear information on individual leads on its own. You need to have actionable purchase intent insights and comprehensive behavioral data for its effective functioning.
Addressing the False Positive/False Negative Issue
You have large number of people visiting your website, consuming content, responding to your outbound efforts. However, there is a chance that many of them are not ready to buy your product/services yet. Therein is a chance of your processes treating them as qualified leads instead of blacklisting them. These are the false positives of predictive analytics solutions.
On the other hand, predictive analytics solutions can assimilate data on metrics such as number of whitepaper downloads, website visits, and registrations for webinars. However, if you don’t provide the correct information, you may end up losing considerable demand. There are still chances of other false negatives, as predictive analytics doesn’t offer insights such as:
- Technologies installed
- Competitors leads are engaged with
- Visits to other external relevant content
- Other subjective interests
This might result in lead generation that is already inclined towards your business and interacts with your lead gen team on a regular basis.
The issue with false positive is that it becomes increasingly expensive as it moves down the funnel without any checks. It can kill the effectiveness of ABM and can hamper your internal reputation, which may create a great rift between your sales and marketing teams.
These issues can be addressed with a cautious approach and right data inputs. To make the predictive model work effectively, you simply require a correlation of marketing automation data and CRM with solid purchase intent insights derived from external behavioral information.
Purchase intent insights are much more than simple content a prospect downloads from your site. Most influential insights appear from the actions of prospects you usually don’t see. When you are able to narrow down the pool of prospects that might be a great fit to those who really exist in the market, you are empowered to make highly informed decisions. Predictive analytics solution is a segment of this process. However, you need to take a hard look at the data sources for achieving better results.