Ways in which data decays
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 including:
In this article I outline the problems of bad data, and show you how you can reduce the effect on your business.
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 $200,000 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through a list broker or through other sources, only to find that a large percentage of the contact data is wrong or outdated.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.
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.
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.
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.
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.
Every year upwards of 25% of contact data 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 project 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.
So, what should you do about it? The approach needs to be split into two:
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.
If you have Microsoft Dynamics CRM, much of this can be done using DQ Global Perfect and Merge.
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 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 CRM 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.
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 CRM. Users will 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:
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.
For any assistance with improving the quality of the data in your CRM or any other challenges with your CRM, please contact Opsis on 02 8212 3480. We would be happy to help you with data challenges, or even just a complimentary check-up of your Microsoft Dynamics CRM .
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Opsis is an expert Microsoft Dynamics 365 consulting company. Our focus is your Microsoft Dynamics 365 success - not licence sales or billable hours. As Principal Consultant, Gill oversees all business operations and strategic planning and execution, yet she still believes in offering personal attention to each and every client, so as to understand their needs and offer tailored solutions. We are based in Sydney, with clients in Sydney, Canberra, Melbourne, Brisbane and across Australia. We offer Microsoft Dynamics 365 strategy, Microsoft Dynamics 365 scoping, Microsoft Dynamics 365 implementation, Microsoft Dynamics 365 technical support, Microsoft Dynamics 365 advice and guidance, Microsoft Dynamics 365 training and mentoring.