0

For sales leaders the value of data is self-evident. It is their window into the team’s sales process, and how they know whether the high-level guidance they are bringing is changing things for the better.

For reps, though, the value isn’t so obvious. “Better data quality” may sound like an awesome high-level goal and look great on a deck, but it means next to nothing for the men and women on the phones. Sure, data is nice, a way to keep track of what they are doing, but what really matters is action: calls, demos, meetings, closes. If getting the data perfect in their CRM gets in the way of these actions, it is definitely the data that will come a distant second in their thoughts.

Start With Something Specific

This is the first mistake that many sales leaders make. They round up everyone at a Monday meeting and tell them that they’ve all got to improve the quality of data in the CRM. Then they are surprised that by Friday nothing has changed.

Of course it hasn’t. “Better data quality” is too large an objective, meaning that individuals tasked with doing this will just ignore it because it’s too difficult to hit. This is the equivalent of saying “be better” to a rep, yet a sales manager would never give such vague advice in terms of sales coaching. Data quality should be an integral part of sales coaching, with leaders showing the specifics of where a rep can improve to help themselves and help the team.

This means 2 things:

  • Making reps accountable for their data
  • Giving specific feedback on what they can change

The first point is solved by making data quality a part of your hiring and onboarding process. By starting from these first principles, you’ll already be ahead and getting the data you need. The reps you are hiring should be organized and detail-oriented. During their ramp up process drill in the importance of getting the data right. Get them understanding that they have to live inside the CRM, so they’ll input the data the moment the sales interaction is made, increasing accuracy.

The second point is dealt with by using the current data and looking for where each individual rep is failing in recording important information. This could be anything, just as long as it’s specific. Ideally, it should be tailored to the rep so that they can make changes and see tangible effects of their changes.

An example of where a rep might omit data is the reason for a closed-lost opportunity. They might consider this a waste of effort, as the opportunity is closed and gone, so why bother adding any extra data? They are more interested in moving on to the next possible opportunity.

But this is vital information for the rep and for the team. Without knowing why they are losing opportunities, there is no way for them to improve this metric. By breaking data quality down into smaller objectives (“Improve your Lost Reasons recording this week”) the goals become more manageable.

Screenshot 2016-04-19 19.20.10

In this case, without this data, there would be no way of knowing that the team is losing opportunities through timing and lost momentum, and therefore no way of rectifying this.

Pipeline-Benchmarks-Interactive-CTA-Gif

Interactive Pipeline Benchmarks

See how your company stacks up against others in your industry by exploring our
filterable Pipeline metrics.

Open Up Your Data And Reports

A primary reason reps aren’t as concerned with data quality as managers is that, in too many organizations, they never get to see the result of their endeavors. Data goes in one end and then out the other, spirited away to the C-suite in weekly, monthly, or quarterly reports, never to be see again by the front lines.

If your reps can see how data is used, then they are more likely to take ownership of it. If they know that their low quality data leads to low quality reports and ultimately low quality decisions by management, then they will absolutely make sure their data is high-quality, for high-quality reports and the high-quality decisions they need.

This is particularly important for metrics that will impact the rep directly. If a rep isn’t tracking their activity correctly, then reports could show them failing in areas they are not, or succeeding in areas they are struggling. In both cases, company and rep miss out on opportunities to improve.

If a rep isn’t tracking their daily activity—the calls they make, the meetings they schedule, the opportunities they move—then management can have no real idea of whether that rep is progressing or not. They can’t forecast properly and they can’t offer personalized sales coaching to improve the rep.

Here, Marie Curie is out-dialing Neils Bohr. If a sales manager were to look at the real, good data he would think that Marie is putting a lot of effort into her calls, and wouldn’t necessarily think she needs any coaching. But if she were more lax on recording her dials, missing just 10% of them, the scenario would look very different:

More on Data Quality

The Truth Behind The Dirty Data Myth

The Role Of Key Players In Data Quality

Cleaning Up Your Salesforce Data

Dials: Good versus Bad Data

good vs bad data calls

In this case (bad data), it looks like she is substantially lower in call volume than Neils, and in need of coaching. This is coaching that ultimately isn’t going to help. What she really needs is coaching in data quality, using the specific case of her dials to get her motivated.

This kind of bad data entry has further repercussions, not just for the rep, but for the whole team. If both Marie and Neils are closing 6 deals from these calls, then their Dials:Deals ratio will look similar, with Neils having a slightly better ratio:

Dials: Deals with Good versus Bad Data

deal dials

But with Marie not logging all of her calls, it is her ratio that seems far better. In this case, Neils is going to be benchmarked against Marie and found wanting. It is he that will get the extra coaching which might not be needed, and Marie who will miss out.

By opening up and showing reps these reports, that are generated every day or week anyway, you can show them what use this data is and how it effects decisions that affect them. If they know that sales leaders are looking at these numbers to see how to make them into better reps, they are far more likely to make sure they are better numbers.

Show How It Will Change Their Day

The easiest way to get sales reps on board with inputting the data you want is to show how it can have a positive impact on their day-to-day.

The immediacy of this is important. The further in the future the potential benefits for them, the less likely the important changes will be made. Instead, if you can show how improved data collection and quality today will help them close tomorrow, then you’ll have them cramming the data into your CRM.

A great opportunity for this is by analyzing a rep’s current pipeline. As this combines multiple different sources of data into a single report, it is a great example to show sales reps the terrors on underreporting, the sales equivalent of the bogeyman—input your data, or the pipeline will get you…

In this pipeline, everything is as it should be. All the data has been entered, and the team knows where they stand:

What is the current state of our pipeline?

pipeline today

Each circle is an open opportunity, with days until it is due to close along the x-axis, and dollar value on the y-axis. The size of the circle represents the weighted sum of all activity on that opportunity, and the color, red or green, tells whether the opportunity exhibits risk factors or not.

Reps should be focused on the largest red circles, with upcoming close dates. These opportunities will have had the most effort already invested and are at risk of being closed-lost.

Sify Technologies is due to close tomorrow, but an initial burst of high activity has ceased, and the opportunity is now at risk from momentum. This means it is imperative for the owner to make the interactions today, or this could become closed-lost at the eleventh-hour. As this opportunity is owned by a diligent rep who input all the right data, it will probably be saved.

Contrast this with a more careless rep, who hadn’t input every single interaction with the prospect, and hadn’t flagged the possible risk factors:

What is the current state of our pipeline?

pipeline today 2

This now looks like a dead cert, the owner already popping the champagne. A high-value, low-risk, low-touch opportunity—almost perfect.

Then the next day the opportunity is dead. The difference here is just a few extra pieces of information in the CRM, the prospect itself hasn’t changed at all. But that data is the difference between $0 and $26,000 in this instance.

It is examples like this that can change a sales rep from a data-skeptic to a data-disciple.

A culture of high-quality data within sales teams is both desirable and achievable. But it entirely depends on the reps playing ball. If they see no reason to make sure their data is perfect, then they won’t expend extra effort making it so.

For sales leaders, it’s important to not just tell sales reps why their data is important, but also to show them. Use specific cases of where they can improve, coupled with concrete examples of how bad data quality is impeding them, losing both the rep and the company money.

Showing the causal link through data to conversion to revenue is the best way to get reps onboard. When you do, they’ll want to give every ounce of extra data they can to bump up the numbers and make sure team is closing every opportunity.

Recent Posts