Customer data is your business’s most valuable asset but only if it’s robust. Here’s how to ensure yours is fit for purpose!
Unable to personalise email campaigns? Unclear as to how many products a customer has? Unsure how often your customer makes a purchase? Unsure whether Mr D Jones and Mr Derek Jones are the same person? Unable to get hold of a customer on their mobile phone?
Sound familiar?
Having up to date, clean and high quality customer data is essential for any business in the age of personalised experiences. It’s also critical for accurate business intelligence, strategy and operational processes. Whilst it’s embarrassing referring to someone as ‘Mrs’ instead of ‘Mr’, it can be costly sending invoices to an incorrect address. With today’s amazing technology companies can get the data basics right and begin to engage with customers in a personalised manner at scale.
The biggest issue is the lack of quality data. Many companies host data that is inconsistent, out of date, inaccurate or simply missing. Even more worrying is the number of major companies investing heavily in data lakes and big data applications when their base data foundation is so poor.
While poor data is a major headache for companies it poses a more deadly threat given the strict regulations and laws surrounding data protection. Incorrect or out of date data poses a risk to any company trying to meet its legal obligations under national and EU law.
So, what does robust data look like?
It’s customer data that is precise in its accuracy, reliable and timely (data decays over time), complete and relevant (is it still important to collect fax numbers / marital status). How this data is kept robust is down to how it is managed, governed, updated, accessed and stored.
So, if your data isn’t robust enough how do you fix it?
Well I’m not suggesting you forego big data investments. For most large organisations it is possible to fix the here and now while simultaneously exploring future requirements, data use cases and technology solutions. There are a number of simple steps you can take to ensure you get your data into shape.
1. Assess the state of your data.
Interrogate your data sets. What customer data do you have? What data is missing? Consider building a sample of customers to research the data quality you hold on them. You can simply run a small project to call each customer within the sample to assess how accurate the data is. Remember to correct any mistakes you have. More importantly give your data an accuracy or quality grade then evaluate how widespread any issues might be were you to extrapolate to your entire customer base.
2. Assess accessibility, usage and storage of data.
Evaluate how easy it is to access the customer data you need. Assess how the data is stored, maintained and kept in good condition. It’s important to analyse whether you have inconsistent or unaligned customer records in different places. Many organisations have multiple data warehouses with a single customer’s data spread across all of them. The spread of data is often not linked. In some cases data is duplicated or a single customer appears as multiple due to data identification anomalies. Again give your organisation a score to help quantify where you are vs. best practice.
By following the first two steps you’ll have a good idea on the health of your customer data and how your business is managing it.
Now comes the exciting bit. Improving your data quality and making it future proof.
- Map your data requirements.
Sit down with your strategy and operational teams and identify all the data points you need to have for each customer to enable your business to function in an optimised manner. It could be contact details, product portfolio, survey results, tenure, complaint issue tags and much more. Some of it you’ll have already (whether in good shape or not) and some is required in the future.
2. Consider future data needs.
This is where data use cases come in. Identify what use cases you have now and are likely to have in the future. Sit down with your product and service teams to map out customer experiences you want to deliver. By doing this you’ll easily find the missing pieces you need to run your business. It might be that you come across a use case for loyalty offers which requires you to have some understanding of historic redemption data or future propensity data.
3. Get your customers in on the act.
Encourage them to self-cleanse their own data that you hold. Google and many other start ups do a great job at this. Whether it’s asking you to validate your existing phone number of update your email address the onus is on the customer to keep everything up to date. This will also be a lot easier and less expensive than actively seeking corrections.
4. Get external help.
Technology is evolving at an astounding pace. There are a myriad of tools that companies can adopt to validate data that currently sits in your warehouse and data customers provide you with everyday. You can use data dictionaries, external databases such as post address databases and credit check agencies to minimise errors.
Of course customer data strategy is a complex topic and will take up a lot of energy on the ground to get it right.
My one plea is for executives to stay excited about future opportunities while not wilfully ignoring the poor data foundation on which they run their business.
Those companies with high quality customer data will outperform those that lack it. They’ll be better placed to offer hyper personalised customer experiences. Experiences that differentiate, drive down churn, improve retention and offer a return on investment.
At Strategy Activist we collaborate with clients to cleanse their customer data while building a working data framework that is fit for purpose. Before you invest in big data, data lakes or a data renewal programme speak to us first. To learn more about how we can help visit www.strategyactivist.com
