Doug Camplejohn, Founder & CEO, Fliptop
Marketing automation has done a great deal to eliminate the grunt-work of marketing. Applications from vendors like Eloqua (Oracle), Marketo and Pardot (Salesforce) make it possible for a small team to manage multiple marketing touch-points like email marketing, social media marketing, landing pages and promotions in one place.
However, while these systems are great at gathering data and automatically triggering marketing actions, the lead scoring portions have limitations that can make marketing look bad and widen the trust gap that already exists between sales and marketing.
That's because in marketing automation scoring, someone on your team has to assign a numeric value to an email click, white paper download, webpage visit, or any number of other marketing activity touchpoints. Those activity scores are added up for each lead to equal that lead's overall score.
And because the score assigned to each marketing activity is just an educated guess (e.g. email click = 5 points, white paper download = 10 points), the final lead score can be way off. This can lead to sales teams focusing their time following up with high scoring leads that do not convert. In turn, they ignore the score and negate the potential value that lead scoring can have.
This is where predictive analytics comes in. By mining the past, you can help predict the future. That's how companies like Amazon became great at predicting what you want and when you want it. Amazon gathers past purchase data, wish lists, similar purchases and customer ratings to predict your future shopping patterns. With a database of more than 89 million active users, there is plenty of historical data to crunch.
You may not realize it, but you already have a similar treasure trove of data being captured in your internal sales, marketing and support systems. By combining that internal data with publicly available web data, you can make Amazon-like predictions and deliver immediate gains for those front-office teams.
In the past, leveraging this data required expensive custom systems and consulting projects. But the rise of cloud infrastructure and cloud applications has enabled a new generation of systems to emerge that can do this for you quickly and at a fraction of the cost of previous one-off systems.
Answering Questions with Data
Because most marketers are not doing any form of predictive analytics today, the potential for upside is huge. Imagine if your business could definitively answer the following questions:
1.Which leads should marketing send to sales, nurture or discard?
2. What accounts or contacts should sales be prospecting with next?
3. Which programs should marketing scale back or double-down on?
4. How likely am I to make my quota this quarter?
5. Which accounts are ripe for cross-sell/up-sell?
6. Which accounts are most likely to churn?
In the absence of predictive analytics, these questions inevitably are answered by HIPPO – the highest-paid person's opinion. Those who operate by gut instinct are at a distinct disadvantage in the marketplace against those who leverage data.
The Price of Bad Decision-Making
Predictive analytics has proven essential in the fight against the high cost of bad data and the bad decisions based on that data. Recent studies have shown that:
•As much as 50 percent of a typical IT budget goes to "information scrap"
•Bad data results in a loss of 10-25 percent of revenue every year
Data and integration expert Hollis Tibbetts at Artemis Ventures put the problem of bad data in perspective this way: "Bad data is a $3.1 Trillion problem for the U.S. economy. It's twice the size of the Federal deficit."
The Future of Marketing Automation
Marketing Automation is still in its infancy, with the majority of users in the technology vertical, using the platforms as a glorified email marketing engines.
The future of marketing automation, and other enterprise applications, for that matter, is to move beyond just gathering data and automating actions, to the leveraging of data for true insights and competitive advantage. Once this treasure trove of data can be unlocked by the majority of enterprises and employees, and not just the few who have the financial resources to afford and analyze it, the impact will be widely felt.