Despite the fuss that marketers have kicked up about big data, there’s been significantly less enthusiasm from their sales counterparts – surprising, since data can provide the same actionable insights for sales that it does for the teams optimising their marketing strategies. Consider data-driven personalisation for example. When tech-savvy salespeople use their databases to gather the appropriate contact information, they’re able to construct a more complete picture of their individual prospects. And when they have a better understanding of their prospects, they can engage with them on a level not previously possible.
Why do you need quality data?
This approach to sales rides the same changes in buyer behaviour that have driven digital marketing for the past 15 years. The modern buyer has almost no tolerance for impersonal and irrelevant messages, and the tailored approach made possible by big data gives salespeople the personal connection needed to convince a prospect that a message is worth listening to.
At this point, you might be thinking that data-informed selling sounds very nice, but since good ol’ sales intuition has served you well so far, you have no intention of changing tactics now. That’s okay, but don’t think for a second that just because you opt to not leverage data for greater efficiency, that your competition will do the same. Many other businesses will see a competitive advantage that data can give them:
- Higher engagement rates
- Better quality leads
- Time recouped for more productive activities
- Smaller, more manageable data lists
How easy is it to compete with businesses that can do what you do but faster? My point is that if you want to hang on to your market share, you need to get scientific about sales now.
What happens when you use poor quality data?
Of course, if you use poor quality data – either because you have neglected to update your database or because you have bought a cheap generic data list – your sales efficiency will drop. Campaigns that rely on inaccurate and irrelevant data lists see time wasted on wrong number calls and pitches to people who have no need for your product or service. And then there is the detrimental effect it has on your ability to plan initial calls. You could have the right contact information, but still have inaccurate data on a prospect’s preferences, needs and challenges. Such data would result in poorly planned starter conversations that fail to gain traction.
But, that’s only if you are using poor quality data to support a telemarketing campaign. If you use bad data in an email marketing campaign, you could find yourself with an even greater handicap. An ISP will tolerate only so many bounced emails and unsubscribes before blacklisting an account, and poor quality data is the fastest way to put yourself in their crosshairs. So, avoid using bad data if you don’t want to get one of your most important outbound channels shut down.
How do you use quality data to close more sales faster?
Step 1: Prepare the data you need for a campaign
The first step is to divide the data into separate lists for each of your target market segments, omitting any contacts that don’t belong in these groups. Then, determine what kind of information you need to better engage your prospects. Once you know what you need, compare your required data to your current data while also determining the accuracy of your current data list. When you know how accurate your data is and what information is still missing, you can decide how to improve it. If your data is only outdated, you can simply cleanse it, but if it is missing large chunks of information, you’ll need to enrich it with extra data fields. A good data vendor will provide both services.
Step 2: Put in place processes and structure that dictate the strategic use of data
Start by grouping leads according to lead source, title path, industry, company size and sales tactic. This allows your reps to prep once for calls to an entire group, instead of prepping for each call from scratch. Some individuals might require a slight adjustment in tactics for greater personalisation, but these tweaks don’t require nearly as much time as if an agent had to prepare each separately. Once you have grouped your contacts, you can implement a call cadence to dictate when and how frequently calls, voicemail messages, and emails are sent.
Step 3: Collect and analyse performance data while continually improving client data
At this point it would be useful to think like a data scientist. Track your sales metrics to determine what times, frequencies and appeals deliver the best results, and adjust your strategy accordingly. Get into the habit of A/B testing and collecting performance data, and when you have to improve your client data be prudent in your choice of treatment. To learn more about data enhancement, you can download our data guide for more information
Image Credit: CargoSmart
