Why you should invest more in your data
Over the last 10 years, there has been a growing recognition of the true value of data, leading to the headlines that “Data is the new oil!”
This perception is primarily focussed on the existence of the data but not necessarily the quality of the data. Without quality, your data is nothing. In fact, I would go as far as to say that, without quality, it can be downright counter-productive!
The impact of poor data on your CRM system
Think of your CRM as a Ferrari and your data as the oil that lubricates that Ferrari. Given the right ingredients, like a Ferrari, your CRM can take you places very quickly. If you neglect to service your Ferrari and allow the oil to become contaminated, pretty soon it’s going to start to struggle to deliver the same performance, and ultimately seize up.
It’s the same with data. Your data is the lubricant of your CRM and helps keep it running at peak. Neglect the quality of the data and your CRM will struggle to give you the returns that it is truly capable of.
In real terms, what are the symptoms of poorly managed data and how does this impact your system?
There are many symptoms that exist, some obvious, and others more subtle. Let us consider some of the more obvious ones.
Data inconsistencies are the key factor that lead to a proliferation of duplicates within your CRM. These inconsistencies extend from irregularity in how a company name is recorded through to how a person’s first name is lodged, such as the case of “Tony” v “Anthony”.
The nature of applications is that these inconsistencies remain hidden and only ever emerge when the user is proactively searching. Once they are revealed, CRM users rarely fix the issues immediately as they are both time-consuming and tedious. And, if they did, would they be doing it the same consistent way as every other user? I think we all know the answer to that question…
Poor data segmentation is the scourge of CRM’s! Even the most basic CRM is designed to enable you to do highly targeted marketing. It’s what they were designed and built for. Good data segmentation, however, relies entirely on the subjective choice of the user. For instance, take Industry coding. Most systems provide a pick list facility to ensure some data consistency, but, at the end of the day, it’s an entirely subjective choice by the user, which is why there are often so many conflicting choices. This plays havoc when creating marketing target lists as many out of target organisations get included and good ones get left out.
Missing targeting values, such as Role descriptions, also play a big part at the contact level and determine if a person gets targeted or not. Organisations are comprised of people and at the end of the day its people that make decisions and not organisations. Fail to target the right people and your whole campaign is diminished.
The impact of poor data on your market
Talking of people, trust in digital communication is at an all-time low. It is lack of trust that has your carefully crafted marketing message consigned to the trash can without a moment’s thought. Lack of trust is driven by factors such as clumsy salutations, for example – “Dear L” or “Hello [name missing]”. These types of greeting give the game away that you, the recipient, are part of a flawed automated process and that you didn’t get that communication thoughtfully prepared by an actual person. And, if it was, they are clearly an idiot, so why would you want to deal with them?
There is one other vital behaviour that is also in play – time poorness. Most users are struggling to cope with their Inboxes, and they want to clear them as quickly as possible. If something looks odd, they are very quick to determine that it could be a scam and are only too pleased to use that as a good reason not to invest any time in evaluating the contents.
The overall effect of this is that you lose potential prospects and, in turn, this depresses your sales volume – by up to 12%.
Yes, 12% of sales are lost through the effects of poor data!
The upshot of all this, is that poor quality data is actually less than nothing – it is, in fact, counter-productive.
What are your options? A big-bang approach to fixing the data quality issues would be nice, but, given that data decays at the rate of 70% per annum, you are wasting your time and money if you don’t introduce an ongoing data quality program that does more than just cosmetically improving the data.
Whatever you implement, you need to make sure that it gets your data fit for the purposes you define. A useful analogy is you can get your car detailed and it will look great, but you won’t know if the car will get you from Sydney to Perth unless there is a full mechanical inspection and remediation.
Getting to a continuous data quality improvement regime requires a staged approach.
The first stage is getting your data assessed and measured against the key benefits (in marketing terms) that it needs to deliver. In knowing what the deficiencies are, an intensive data cleaning exercise can be designed to get your data to the level of fitness it needs to be. Think of it as a Data Bootcamp. In designing your Data Bootcamp, first determine the level of Data Fitness you need, as this will reflect in what you need to invest to achieve it. Why invest in getting your data to Olympian level if you just need it to run 1 kilometre every day?
Lastly, create a Data Policy that sharply defines how you want your data to comply and implement a repeatable process that ensures it is kept in trim on a regular basis. Successful marketing should be causing your data to be continually polluted and, without a continual data compliance process in place, your data will very quickly degrade back to where you started.
Written by: Andy Kyiet, CEO, Marketing Data Fitness – www.datafitness.com.au, E: akyiet@datafitness .com.au. M: 0402 456 027
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