CRMs are supposed to be used to achieve better efficiency. By investing time in the CRM, sales teams should be able to identify leads, retain existing customers and successfully recruit new clients to the fold.
Your research and development team should be able to use the metrics from the CRM to drive next year’s products, and the marketing team should be able to feed this back into their campaigns next year.
CRMs are supposed to be used to achieve better efficiency. By investing time in the CRM, sales teams should be able to identify leads, retain existing customers and successfully recruit new clients to the fold.
Your research and development team should be able to use the metrics from the CRM to drive next year’s products, and the marketing team should be able to feed this back into their campaigns next year.
More Read
That’s great, in theory.
Sadly, many CRMs fail to perform well. No system could feasibly solve every problem in your business, but if the CRM is creaking under the weight of dirty data, it could actually be hindering progress.
Not an Afterthought
We’ve heard estimates of CRM project failure rates between 30 and 60 per cent. And when you consider that the cost of a CRM starts at hundreds of pounds per month, the potential for waste is enormous. That’s not counting the cost of implementing the CRM, including change to systems, processes and workflows.
After all of that cost and investment, it can be extremely disheartening to find that staff simply don’t trust their data.
The greatest success in data quality comes from good planning. Unfortunately, many businesses do not have the experience to plan effectively, or do not realise the havoc that dirty data can wreak. But if you’ve not yet embarked on your implementation, there are lots of practical steps you can take to ensure a good standard of data quality.
Our recommendations are threefold.
- Data preparation
Ensure the data you are putting into the CRM is clean, valid, deduplicated and fit for purpose. Putting dirty data into a brand new CRM is like forcing a square peg into a round hole. The two just don’t fit, and it isn’t worth trying to force them.
Good preparation is critical if you are to inform other parts of the business. You cannot create meaningful or reliable reports, or formulate accurate intelligence, if your data is not cleansed, filtered and structured correctly.
- Finding patterns
Look at systematic failures and find ways to improve processes prior to CRM roll-out. You may find that data capture methods are broken; you may discover a flaw in another application. This can help you weed out problems that are spawning new dirty data, adding to the problem you’ve got.
- Using self-service tools
By adopting data quality tools at an early stage, you can use self-service methods to prepare your own data, improving productivity as the implementation progresses.
Fixing a Broken CRM
If your CRM is broken now, and your data is decaying, things are only ever going to get worse.
Think of your dirty data like a bad debt. You’re going to need to invest in the quality of your data; repay the interest you owe. The longer the data is neglected, the more you will owe, and the more the interest will be compounded. In a few short years, your data debt could be so great that you’re left bankrupt of any meaningful information.
Often, well-meaning advancements in data management can cause more problems than they solve. For example, we might import data from another system to try to replenish our faltering CRM. But if that data isn’t properly prepared, you could end up with invalid entries that can’t be opened or saved, or cause the system to crash. If you continually leave bad data at rest in the database, you will reach a stage where no single record can be relied upon.
If your CRM is not fuelled with good quality data, you must:
- Fix the data flowing in so that it’s fit for purpose
- Invest in the stagnant data that is already rotting within
Neither of these things need be tedious; neither need be a wholly manual process. Data quality software can help you to polish the rough diamonds in your database, resulting in data that is current and fit for purpose.
Proper data management means finding gaps in your data, eradicating invalid data, and removing data that is duplicated or out of date. This focus on data quality must be broad enough to encompass the whole organisation, yet fine-tuned enough to pick up phonetic matches of someone’s surname. Data quality software can compare millions of records each minute to achieve this.
The Way of the World
The ultimate goal for many companies is the single customer view, a state where every customer is represented by one comprehensive database entry. Without a mature and managed approach to data, the single customer view will always be pure fantasy.
If you don’t clean up your CRM now, what will happen?
- Integrations between systems will fail
- Staff will become frustrated and disillusioned
- Customers will lose contact and drift to competitors
- Waste will build and build
- The data dream you had when the CRM was first implemented will be nothing more than a memory
Respected think tank Gartner says that the market for data quality tools is growing. It predicts that businesses will spend $2 billion in 2017. The pursuit of pure, usable, efficient data is a goal shared by businesses globally, and it’s a goal we must all realise if we are to compete effectively in the years to come.