Currently, the phrase ‘Big Data’ is everywhere. The phrase has been in use since 2013, and as forecast, ‘big data’ has been a hot topic since 2014. Larger organisations are beginning to get to grips with ‘big data’ whilst most smaller businesses have yet to make a start. To complicate matters, ‘Big data’ has now evolved into ‘good data’ and ‘bad data’, so If ‘data’ comes under your remit, there could be an overwhelming project to tackle.
Recent research undertaken by Experian Data Quality reports that 88% of a company’s bottom line is impacted directly by bad data. The average company loses 12% of its revenue due to the lack of quality data in their possession.
With the above in mind, no amount of automated marketing, segmenting your database or overhauling of your Customer Relationship Management system (CRM) will produce the required and/or improved results if your data is not “good data”.
Furthermore, bad data can impact customer loyalty, and consequently the reputation of your organisation. We recently discussed our own experiences in the office in terms of being on the receiving end of marketing campaigns that hadn’t cleaned their data as well as they should have. For example a colleague recently purchased some shoes for her son, from an online retailer. The order arrived as expected, on time and in full, a seamless transaction. However a few days later, she received an email from the male only clothing/online shoe retailer, suggesting she could be the new male model face of the company! My colleague laughed and may well be investigating a new line of work, but others may well have taken offence to being taken for a man. Get your customer’s gender incorrect and you risk losing a customer, their loyalty and revenue.
This is just one example, and while this is a B2C example, we’ve heard numerous B2B examples of emails going to the wrong databases and sensitivities not being taken into account, and marketing reporting being wildly inaccurate.
What is ‘bad data’?
If your data has any of the below characteristics, it is classified as “bad data”:
- Violates business rules
- Without a generalized formatting
- Incorrectly punctuated, or spelt
So where does this ‘bad data’ come from?
Across most organisations, data is collected from a number of sources. From those organisations which were surveyed, the follow sources were cited as common data collection points:-
- 47% from apps and mobile enabled websites.
- 54% from call centres.
- 60% via their sales teams, i.e face-to-face meetings.
- 73% via their website.
What is ‘good data’?
‘Good data’ conversely is all 5 of the following:
- time stamped
- industry standards based.
Without all 5 elements, data quality is not considered as good, so you can see why “bad data” is common!
Why spearhead ‘good data?’
There are of course, a number of obvious answers; for legal compliance, such as the upcoming revised EU Data Protection Laws, or to reduce the risk of fraud. But ‘good data’ can be beneficial in a number of ways.
‘Good data’ will help support your company’s brand and reputation as well as enhance the customer experience, each and every time your customer engages with your company. Simply getting your customer’s name right and ensuring their address details are updated when they move office, all help support your revenue stream, your brand and your reputation. The old adage remains true; when a customer has a bad experience, they tell between 8 to 10 individuals. When the customer experience is good, they tell between 2-4 people. With some much competition, companies cannot afford to make errors which can easily be avoided. Small errors can have a big impact.
Cost savings and increased efficiencies are also a by product of ‘good data’. Once your data is complete, consistent, accurate, time stamped and complies with industry standards, you will benefit from not having to clean, review and structure data each time you run a campaign, saving time, resources and ultimately reducing the cost of each campaign.
‘Good/clean data’ is imperative if you want to start segmenting your database and personalising communications with your customer. For example, you don’t want to include existing customers in an email campaign clearly aimed at enticing new customers. The company may appear disjointed, and that Marketing and Sales are not communicating, existing customers are unimportant and so on.
When analysed, ‘good data’ can also provide teams with extensive insight into customer behaviour, in order to help them make more informed business decisions, which may lead to further efficiencies and increased revenues.
Identifying ‘bad data’
Ask yourself a few simple questions. Who is responsible overall for your organisation’s data? Does your organisation have to review, edit and work on your data for each campaign you create? 38% of companies reported this is a manual process. Are campaign results fed back to Marketing? Are your campaigns generating leads? 42% of organisations reported that inaccurate contact data creates a barrier to multi-channel marketing.
How do I make my ‘bad data’ ‘good data?’
Achieving this is all part of a wider process. The quality of data that goes into your workflow, will determine the quality of the data output. You’ll need to start at the beginning of the data capture process and work systematically through the data workflow, engaging with your organisation’s touch-points at each stage. You may need some external support to do this, certainly in the first instance. Going forward, a structured, defined process will help tackle issues and maintain the quality of your valuable data.
What outcomes can I expect from having ‘good data?’
The benefits of having ‘good/clean data’ are numerous and importantly, these business benefits are measurable. By undertaking a Marketing Process and Data Improvements project you will know that your data is complete, consistent, accurate, time stamped and meets industry standards. Thus you are mitigating errors, making data segmentation easier and importantly are working within the law.
You can create a segmented database once and use it often, saving company time. Reporting is easier and more automated, and analysing data enables management to make better decisions for your organisation (almost 48% of companies use their data for decision making). Having automated data and workflows also saves time.
More directly measurable results that you can expect from a Marketing Process and Data Improvements project include clearly demonstrated value added by your Marketing Department. Better and more accurate reporting on lead progression at contact and company level for example, enabling Account Based Marketing. The more you can slice and dice, and rely on your data, they more sophisticated your marketing and marketing reporting will be.