A recent survey by Experian Data Quality found that an average company is losing 12% of revenue because of bad data. This comes from roughly a quarter of information in critical business systems believed to be inaccurate in many organisations. This is costing companies in many ways:
Inaccurate data is affecting the bottom line
77% of companies surveyed believe that their bottom line is affected by inaccurate and incomplete contact data and on average, respondents believe 12 percent of revenue is wasted. This waste comes from several different areas.
Many organisations lose as much as 27% of selling time by the use of bad prospect data. If you have a team of 10 sales people spending a quarter of their week working on prospecting, this could easily be $20,000 per year.
One of the biggest causes of the lack of ROI (return on investment) for lead generation is the quality of the list – garbage in – garbage out. The reasons for not investing in validated data are many. The manager may feel the need to start calling to drive quick results. Perhaps the manager acquired a list through a list broker or through other sources, only to find that a large percentage of the contact data is wrong or outdated. Commonly, managers skip the data investment to save money. They opt instead to put budget dollars into the calling program, assuming that investing in the campaign structure is the best way to generate campaign ROI. In fact, a poorer quality campaign to higher quality data will usually yield better results.
However, changes in business practices have brought about new consequences. Some relate to customer engagement and loyalty programs that have made a strong surge in the past few years. 84 percent of companies have a loyalty or customer engagement program. Unfortunately, 74 percent of respondents have encountered problems with these programs. The main causes are inaccurate information, not enough information on the consumer, and an inability to analyse customer information. All of these issues relate to data accuracy and accessibility.
Another trend is business intelligence and analytics, frequently referred to today as big data. 89 percent of companies now use their data in a strategic way for business intelligence and analytics. If the data is inaccurate the conclusions drawn from the data will be incorrect.
And if you have not made the jump to these systems, for data that is held in a spreadsheet, one cell in sixty is often incorrect. For a spreadsheet comprising 100 columns and 2000 rows, which is not large by modern standards, over three thousand cells probably contain inaccurate data. If you are still relying on a plethora of spreadsheets to manage your business, perhaps now is the time to think about moving to a CRM solution.
Bad data gives you a bad reputation
A database with no data has no value. The value of the database comes from the data that it holds. This data can easily make it vital to company operations.
Lack of correct phone number for prospect. If you do not have a contact number for a prospect your sales team will either give up – lost opportunity and if the prospect was expecting the call a bad feeling, which may affect future opportunities with them. Alternatively the salesperson spends time googling to find the number, or more likely, ringing the switchboard to find the correct person. This ringing around does not leave you looking good in your prospects eyes.
Not knowing the prospect's time zone It is often not possible to tell a time zone from a phone number. If the data does not explicitly have this, and the salesperson does not spend time confirming it you run the risk of waking someone up in the middle of the night. A call made first thing from Sydney to a prospect in Perth, and that is not international, could easily disturb those last precious minutes of sleep and create an unfavourable impression.
Repeatedly contacting the same person. If you are emailing back as a result of an online enquiry, two people in your team could send very different messages and leave your prospect confused. A confused prospect is not likely to get back to you.
If emails, phone calls and other client and prospect interactions are not logged in CRM and then checked by other users prior to approaching a prospect you run the risk of two different people selling to them same person. This is high on the list of unprofessional behaviour.
And if this person is already a customer, it is even worse. Once someone is already a customer they should be honoured and nurtured as such and not ever treated as a prospect.
One of the largest issues for CRM users is the bad data.
Every year upwards of 25% becomes inaccurate, because people have moved on or changed positions. This is probably no surprise to you. .
One survey showed that bad data is the second biggest issue. Bad data is one of the top reasons given as why CRM projects fail. Accurate information and reliable reports from the data are the lifeblood of an organisation. An empty database has no value. The value of a database comes from the data that it holds and manages. Without this, management does not have the wherewithal to make good decisions and sales do not have the tools to turn prospects into customers.
What should you do about it?
The approach needs to be split into two parts:
First, clean up the data that you have This requires finding and merging the duplicates in your data and then analysing the remainder to see what are the common problems that can be solved. Once this stage has been completed, review the remainder and work out the priorities for data cleansing. Focus first of all on the data that is most frequently used and is most visible – addresses, phone numbers and email addresses will be the most important for many organisations. Then focus on business specific information such as price lists, industry, relationship with you for each account (if you are a B2B organisation) or contact (if you are B2C). Once the bad data has been removed, augment the data in the CRM or ERP with information that will give your sales people and management the edge. This can be done by adding third party solutions such as Riva Insight to glean additional information from social media.
Reduce the probability of bad data returning This requires looking at how the data became bad in the first place. Data is either bad because it was entered incorrectly or because it has changed since it was entered. Reducing the effort required to enter data will help get more accurate data into your CRM. Users never enjoy spending time entering data. So to keep the data as clean as possible, we need to make the data entry as easy as possible. This can be achieved in several ways:
Look at the data ownership and ensure that records can only be updated by people who have a vested interest in the data being correct. The data can then be viewed by anyone else who needs to use the information.
Link fields in your CRM, so that where possible fields autofill based on the data entered elsewhere.
Add automation so that fields can be auto-filled, based on other data in the system.
Add data quality controls so records can only be saved after a certain amount of data checking has been done.
Define processes for data loads, for example from purchased lists, so you do not pollute your wonderfully clean data with a load of dirty data.
Confirm that your picklists or dropdowns have all the relevant values in them and that users know when to use each of the values. I recently heard an interesting story about the Industry list in an organisation’s CRM. Because the exact meaning of this field had not been clarified and communicated, it was being used in different ways. An example of this was two accounts both tagged as aeronautical; one was an aeroplane manufacturer and the other was a flying school. Obviously the needs of these two organisations would be very different. But this could not be seen by their tagging.
Review the text fields and see if they can be changed to more appropriate field types. Test fields are the easiest for data entry, but this ease also creates problems because of the lack of rigidity.
Integrate the systems, so that data is entered once and flows through the system to where it is needed. This removes the double (or triple) entry of data. And integrating a customer facing website means that the effort of data entry can pass to the client or prospect who has something to gain from entering the data correctly. Integration used to be a massive headache. With modern CRM solutions this is no longer the case.
Create dashboards so that users can see where they need to increase their efforts in data management.
Make data quality one of the metrics that you measure and manage. Your CRM should have reports that will help with this.
Train users so that a consistent way of entering data is maintained.
And finally, remember that data cleansing is not a one-off job. Yes, data needs to be as good as possible before it is imported into CRM, but it also needs to be maintained. Nowadays customer data changes at an inordinate rate – 25% becoming inaccurate each year.
How does your data stack up?
If you would like a free personalised report on how well your marketing data stacks up against the rest of the industry, then click the following link to complete the Demand Flow Intelligence survey.
About the author
Gill Walker is head of Opsis which provides companies with customer relationship management advice, consultancy, solutions and services based on Microsoft Dynamics CRM. Opsis is the only Sydney based consultancy with over a dozen years experience with Microsoft Dynamics CRM.
