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How my Love for Predictive Marketing and Sales Developed

I worked under the Salesforce umbrella for a little over 2 years after the acquisition of Pardot. During the acquisition, the sales department transitioned to a territorial model. We had recently been following rules of engagement based off of last activity touch. We also transitioned into Salesforce’s own live instance of it’s CRM and in turn, inherited a very large database of prospects to go after.

Like most companies, the territory was based off of a specific location as well as size. Sales reps were spread across all areas of the world to try to go after everyone possible. Often true of large companies, we were fortunate to have a lot of reps which led to many touch points across the board.

The mid-market segment was considered the best market to go after. The senior AEs were promoted up to this group of reps and were given accounts that they could hope to turn into large dollar deals. However, there weren’t as many as found in the smaller sized segments. This is why targeting was key.

Targeting is Key

I had one rep that was very analytical in her processes. She spent many hours researching every one of her 250 accounts to find out all she could know about the company and personas she was after. This ranged from the technology found on their website, if they took on funding, founding date, etc. Not only did this help her prioritize what to go after, but also helped her target and personalize her messaging.

One thing that always left me feeling puzzled was the amount of time I saw being utilized for this. It was a struggle to decide how to calculate the value of the time spent on each account and if the research was conclusive in finding the highest deal sizes, lowest sales cycles, and highest win rates overall.

I remember using the same tactics observed from my team members in my own efforts to try to grasp our best chances of success. I would use tools like Sales Navigator, Crunchbase, and Ghostery to name a few. Each tool had a few important data points so it was an incredibly manual and tedious process to look up each one and try to gather as much information that could possibly be of value. That could possibly be of value...How could I know what would be of value? It was gut. There was no weighting. There was no analytics or customer trends to back it up nor were there any to monitor for adjusting moving forward. There had to be a better way.

Predictive Account Scoring

I first was introduced to predictive account scoring while working at Marketo. We were in the middle of a big push within our outbound efforts and each rep was scrambling to find the accounts that would lend to the quickest ROI. Cold calling was required but strategy around this was key. Marketing wanted to help with these efforts, so they invested in a tool that tiered the accounts in each territory into three different categories. The tier 1s were the best. These correlated the closest with the current customer base and led to the highest deal value. With Marketo’s desire to increase Lifetime Value (LTV) and Annual Recurring Revenue (ARR), this was the best next step to get the sales team to that goal. Icing on the cake was that marketing could also target at the same time with personalized campaigns which led to not only quicker conversions but better marketing and sales alignment.

Predictive Intelligence At Work

Predictive intelligence was also used in territory alignment. Each year, the directors were provided a new group of territories to choose for their team members. To make this as fair as possible, Marketo used predictive tools to build different profiles based on location, how many were vended, industry, and other criteria that were important to gaining the highest ROI. The predictive tool also allowed the directors to see a breakdown of average win rates, deal size, and sales cycle per territory. This would be monitored throughout the year for revisions during the next territory planning.

Predictive is the Solution

Predictive was and still is the solution to my problems and something I and my team members could have benefited from years before. It saves us time in finding the best chances of success. It decreases all the tedious work of finding every data point from so many different costly sources. It helps me monitor progress and continually improve in increasing revenue in shorter time frames. And best yet, it increases the overall lifetime value of every customer my team closes for our growing company. Overall, I am just happy that I discovered Predictive Intelligence and excited about increasing revenue for years to come.

Hear more from Brittyn in the Predictive Guide to Sales here.

Predictive sales ebook promo
March 13, 2017
Brittyn Matthews
Sales Data

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