14 Reasons Prospecting Data Can Fail

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14 Reasons Prospecting Data Can Fail

As a B2B business, you have probably been contacted more than once by an information provider promising lists for greater insight and sales efficiency. Some of the more popular providers include Hoovers and InfoUSA, both of which offer enormous databases. There are also industry-specific companies, such as CoreLogic, that sources data solely about building permits. Then there are publishers like Randall-Reilly that survey and collect data about specific audiences from the industry’s publications.

B2B prospecting data typically consists of companies or contacts that are good prospects for a business. The lists are chosen based on demographics, firmographics, or some sort of business behavior. This data is typically implemented to increase sales, whether by having sales personnel talk to these better qualified prospects or through target-marketing prospects more likely to respond.

These information providers typically offer you real benefits, if the data is properly applied. However, these are the main reasons this data can fail to produce the expected boost in sales:

1. Sales staff prefers to target existing customers – Your sales goal might require half of the business to come from new customers, but the sales staff know the juiciest fruit is repeated sales to existing customers. Also, your sales staff may not need data for repeat customers because they maintain constant contact with them. If this happens, it may be time to have a separate sales staff handle your existing clients and have your best salespeople target only new accounts.

2. Sales staff has too many support duties – Your sales staff may have so many support duties, they have little time remaining to prospect. If they aren’t prospecting, they aren’t using your data. If this is the case, existing clients can be moved to a client service or retention team to clear the sales staff to strictly find new business.

3. Manual CRM data entry – The sales staff is asked to contact names on the list and enter the names in a CRM. Sounds easy until they realize the data resides outside of the CRM. This means the sales staff is manually entering the prospect data, which becomes very tedious. A typical CRM can easily require five minutes for manual entry of an account, contact, and call notes. You’ll need to plan how to import the data into CRM, as well as ensure you don’t create a load of duplicates in the process. An experienced data manager can help with this.

4. Lack of training – You’re asking the sales staff to use a newly acquired data set, but they may not understand why the data set will improve their success rate or what behaviors to change to take advantage of the data. When training the sales staff on B2B data, it’s important to show them how their daily activities will change once they have the data.

For example, it could be as simple as telling the sales staff, “Rather than driving down the highway to find companies, you will now use the new list provided. It is expected to increase your close rate by 50 percent.” Also, you’ll need do initial training, as well as refresher training at least once per year.

5. Disregarding vendor expertise – A good data salesperson will ask about your goals for using the data. They will have heard many similar applications as yours and advise you on optimizing your process. Unfortunately, some buyers won’t discuss their application for fear of competition, but they risk buying data only to learn their process under performs. Be as open as possible and trust the salesperson to help you assess the value of the data to your business.

6. Skeletons in the closet – Employees at any level might have a few secrets that could be exposed by a new data set. These might include failure to call on key accounts, losing major customers, or misrepresenting the current situation of sales or marketing efforts. Be sure you ask enough questions of any salesperson who strongly resists your new initiative.

7. Unknown nuances – Every data source has its nuances due to the way data is generated, collected, enriched, and maintained. Ask the vendor to explain how these activities happen. For example, you might assume poor phone number validity, but upon asking the vendor, you may learn that phone numbers are freshly appended each month and are therefore trustworthy. In this scenario, unless you asked, you may have sworn off using the valuable phone numbers.

8. Not accounting for data quality – Every data source has imperfections. Ask about the three key quality measurements: 1) Freshness (how long ago the data was updated), 2) Completeness (how much of the subject matter the data covers), and 3) Accuracy (how well the data represents the subject it covers). Each of these measurements should be audited and trended.

9. Data mismatches – It’s rare to invest in data that is a total mismatch to the investor, but data will always have some areas in which it doesn’t meet your business needs. Be sure to understand where these areas are and explain them to the data users. For example, you might sell body sculpting machines using a list of businesses classified as health spas, but not all health spas offer a service requiring such a machine. A salesperson may get discouraged if they call too many spas that have no use for a sculpting machine.

10. Data champion lacks time to implement – Data investments are led by a champion within the company who implements the data. It takes far less time to send the purchase order than it does to implement the data. Implementing data notably requires training and retraining staff, constantly answering staff questions, and integrating the new data with your existing data. The data champion must plan time to conduct these activities before purchasing.

11. Marketing can’t adapt to prospect data – The marketing team may be too accustomed to a one-size-fits-all approach or using a different type of data. Integrating data requires customizing your creative collateral approaches and communicating through new channels. For example, you may have an email program and send the same message to everyone. Now, you have defined eight target segments and need to personalize each email by the recipient’s name. You may have to upgrade your email platform, build creative for each segment, and add more detailed results tracking. This could require more time than the team can spare.

12. No user buy-in – Often data can be purchased by a small set of decision makers who will then count on front line staff for implementation. It’s difficult to get your staff to use anything that you merely dictate they use. You’ll find it helpful to have the data vendor demonstrate the data for the end users. This creates buy-in from the same staff when it comes time for them to change their old way of doing business to the new data-driven way. It also reveals any mismatches that must be addressed before proceeding. Lastly, there is always a chance an employee thinks they know the subject matter of the data (“I already know my territory,” for example). Including these employees in the decision process helps to get them on board.

13. Impact too small – A team can only handle one or two major initiatives at a time, and therefore every initiative should have a major impact on the team’s performance. If implementing the data doesn’t substantially boost sales, you should not undertake such an initiative in lieu of bigger projects.

14. No metrics – Without metrics, you won’t know the impact of your data investment. While it’s tempting to push the data to the team and tell them to start using it, first determine how you’ll know if business has resulted from your data investment. If you are using a CRM, a simple method is tagging leads or opportunities with the data source. Then you can later examine your leads and opportunities in bulk to see how much business was won from the data source.

Now that you have knowledge about the possible failure points, you can anticipate the challenges and have a plan ready to mitigate any issues before they arise. Get your data working for you, and you will definitely see a huge boost in sales!

About the Author

David Austin is the product manager for ECI’s Macola ERP and KnowledgeSync business activity monitoring software. Having designed and implemented data and software products, David brings over 20 years of experience building and leading top-performing teams in the construction equipment, trucking, and manufacturing industries. He also has been a team member and leader in sales, marketing, product management, and operation functions for many products that include Randall-Reilly’s EDA, RigDig and Top Bid. David received his MBA from the University of Texas at Arlington.