6 most common causes of dirty CRM data and how B2Bs can avoid them
Dirty CRM data “appears with the tedious inevitability of an unloved season”.
It’s no secret that the data sitting in your CRM is not always in the best condition. Incorrect, duplicate, and incomplete data, constantly plague the system.
Writing to a dead email address, working on duplicate entries, having the wrong time zone, are, at best, frequent frustrations. At worst, they’re revenue-sapping stumbling blocks.
According to IBM, poor data costs the average business $9.7m.
Whether it’s the countless hours Sales reps spend correcting the data, the missed opportunities, or the bad decision making, the cost of dirty CRM data is hard to ignore.
So what are the most common causes of dirty CRM data for B2Bs and how can you avoid them?
In this post, we’ll be answering both these questions from our direct experience with dirty CRM data at Dreamdata.
We’ll cover:
6 examples of the most common causes of dirty data in B2Bs’ CRMs
What this means for the B2B user
How to avoid dirty CRM data
How to really get on top of your CRM data with Revenue Attribution
Here’s another Dreamdata CRM piece.
Let’s dig in!
B2B’s 6 most common causes of dirty CRM data
From Dreamdata’s exposure to our customers’ CRM data (from CRMs like HubSpot and Salesforce), we’ve narrowed down these 6 examples of dirty data. These are:
Incorrect data.
First on the list is incorrect data. That is, data that does not correspond to the field it was entered in. For instance, having the wrong telephone number for the contact, or having a name field containing a date.Incomplete/missing data.
Incomplete data can be either an unfinished field or a null-value entry, i.e. no info added.Inaccurate data.
In this instance, the data is for all intents and purposes correct, but, the information, or some parts of it, may be inaccurate. A typical example would be a contact’s email entry being a personal and not professional one. Or, having the contact’s name misspelt.Duplicate data.
This is another very common cause of dirty data. Many B2B companies, especially those with complex commercial tech stacks, have duplicate customer data across (and within) tools. This is overwhelmingly the result of two or more users inputting data on the same customer.Inconsistent data.
This is data which although in the same field, does not completely match. This tends to be the case when the information is entered or stored in different tools. For example, a ‘name’ field might include both first and second names, whereas they’re two separate fields elsewhere.Treating the CRM as a data warehouse.
The last common cause of dirty CRM data, or perhaps more accurately, a misuse of CRM data, is treating the system as a data warehouse.
Although the CRM is a useful data source, holding valuable customer data, it lacks the versatility of a dedicated, cloud-based data warehouse like Google BigQuery.
What dirty CRM data means for the B2B user
Ok, so what do these examples of dirty CRM data mean in practice for the B2B company?
As set out in the intro, 👆 IBM puts the financial cost of dirty data at an annual average of $9.7m per company.
This loss arises from the sum total of impacts dirty data has on the business. Here are some of the most significant examples:
Dirty CRM data can be disastrous for operations
Lost opportunities
In the cutthroat world of B2B sales, every lost opportunity is a costly one.
The simple error of an incorrect email address can result in losing the opportunity altogether. As the time and effort needed to recover the correct data from the contact, not to mention the reputational damage (more on this in a sec), might prove too great to beat off competitors.
Overlap of outreach from Sales and CS, or even different Account Executives - caused by duplicate data - could result in outreach overkill and losing the customer.
Poor decision-making and missed opportunities
Equally, dirty data can affect the lead generation side of things too. Without clean data, your CRM could be painting an inaccurate picture of who your ideal customer profile (ICP) and persona is.
Without a clear picture, which a CRM with clean data can help paint, you will miss opportunities that are within your ICP and, on the flip side, waste time on dead-end leads.
Decision-making stemming from your CRM can also be skewed. For instance, inflated prospect/pipeline count caused by duplication can lead to erroneous judgements and forecasting.
Productivity cost
In a similar way, productivity, especially for Sales teams, can be greatly hampered by the CRM’s dirty data. Spending time searching for, checking and cleaning dirty data takes time. And time, as the cliche goes, is money.
Oh, and do this for a dead-end lead - also a by-product of dirty CRM data - and you’ve doubled the pain.
Maintenance costs
The accumulation of dirty data also accumulates the cost, in time and resources, of cleaning and maintaining the CRM system. To put it another way, treating the symptoms by cleaning the data and getting to the roots of the cause, generates additional costs.
Reputation
Any and every customer-related mishap, from the minuscule to the catastrophic impact brand reputation.
This is irrespective of your size. If you’re an early-stage startup it might mean bad reviews or negative word of mouth. As a more established brand data mishaps will erode trust.
How to avoid dirty CRM data
Make data quality a priority across relevant teams. Simply having a shared acceptance and understanding of how valuable (and costly) data is and how to avoid erroneous handling is a major step in the right direction.
All you need to know for clean B2B go-to-market data
Identify points in the workflow where data is likely to be ‘dirtied’. Develop and refine processes to avoid this - both manual and automated.
Automate data entry whenever possible to reduce the impact of (inevitable) human error. From automated sales activity tracking over to marketing campaigns and customer communication - every bit of data that makes it into CRM in a structured, accurate and automated way helps you improve your data hygiene.
Introduce maintenance regimes.
Enter and update fields promptly and regularly - especially manual entries. To minimise the risk of forgetting info, you really want to be entering and updating information on your prospects as soon as you have it.
Review your data regularly. You should consider introducing regular data audits to make sure any dirty data that makes it through the net is cleaned out as soon as possible.
Track data from as many sources as possible. Not having data can be as damaging as dirty data.
Some CRMs do offer limited tracking, but really, especially in the B2B scene, you’re better off having a dedicated tool. CDPs such as Segment or revenue attribution tools like Dreamdata offer leading data tracking tech. You can read more about it here.
Integrate your CRM with other tools. By integrating your CRM with other tools on the commercial stack - think ad platforms, ABM tools, and helpdesks - you capture all the data across your customer journey. Leaving no gaps for dirty data.
Check out what this means for B2B Customer Journey mapping.
In fact, it’s through integrating all your customer (and revenue) data from across your stack that you can truly get a handle on your customer data.
Let’s see how 👇
Really get on top of your data: B2B Revenue Attribution
Imagine a world where all your customer data was tracked, collected, cleaned and stored (and analysed, but not relevant at the moment) in one place.
Well, such a world exists, and it’s called a B2B revenue attribution platform.
The data platform sitting behind a Revenue Attribution tool, tracks and collects all the customer (revenue) related data into one place. That means data from your CRM, ad platforms, automation tools, etc. as well as on-site tracking.
Once it has all this data, it transforms it by merging, cleaning and enriching the data - to make sure there’s no duplication or empty values. After which, the data is run through attribution modelling to connect the data to revenue and pipeline generated.
In summary, a revenue attribution platform handles the complete process, from gathering and cleaning the data to connecting all customer-related activities to revenue.
This means that in one swoop, an off-the-shelf revenue attribution platform like Dreamdata takes care of the dirty CRM problem too many B2Bs face.
You might want to check this article on determining whether you need a B2B Attribution Solution.
