Customer data analytics is one of the most important parts of your company’s marketing strategy. Analyzing customer data can help you make better decisions for your business, understand and connect to your audience on a deeper level, develop more effective campaigns, and identify any areas in your strategy that are in need of improvement.
Yet despite these benefits, companies are still finding customer data analytics to be a constant challenge. According to Gartner’s recent Marketing Data and Analytics Survey, senior marketing executives are unimpressed with the results they are getting from their marketing analytics efforts. In fact, they said that marketing analytics only drive about 54% of their marketing decision-making.
If you find yourself in a similar situation, you might ask yourself, what went wrong?
Here are a few common ways that customer data analytics efforts can go wrong, and what you can do to get back on track:
You didn’t clearly define the goals of your analysis. Why do you want to analyze this particular customer data? What are you hoping to achieve or what problem are you trying to resolve? In order to get a good answer, you have to start with the right question. Many companies will invest money in analytics before they have even defined the problem in the first place, much less what they are going to do once they solve it. Some marketing executives think that as long as they have easy access to customer data, they will automatically start making more strategic marketing decisions. But what good is having easy access to data if you don’t know why you need to put it into action in the first place, let alone how to put it into action? Just like with anything in our industry, clearly defined, yet realistic goals need to be established first before any investments of time and money are made.
You are working with inaccurate data. If you have a sales team who is entering data for new leads in your CRM, leave plenty of room for error. Mistakes can happen, resulting in data that is inaccurate or unreliable. Make sure you implement a weekly scrub of any new lead data, meeting with your sales team to review their data collection entries. Communication is key here, as sales teams often lack the analytics context needed to guide their data collection methods. Let them know what you need from them, and if it is possible, use forced formatting settings within your data entry program to get standardized information right from the beginning of the process. Using an automated system can also be your best friend, preferably one that will update lead data any time a new action is made and also send triggered reminders for the various steps your sales team needs to take for each lead.
Your marketing data management team is blinded by agendas: At best, analytic products are scientific and objective, delivering brute facts about your subject so you can make better decisions. At worst, biased analysis selectively gathers and synthesizes only those pieces of information that support a faulty preordained conclusion to lead you down a path of lies. If you suspect that one or more members of your marketing data management team are harboring agendas that shadow their insights with bias, you can ask them for their raw data and cross-validate with another member of your team. On the most important decisions, consulting with an industry specialist or professor can be an excellent way to quietly cross-validate their claims and could be well worth your while. Executives in every industry would be greatly profited by getting some experience making pivot tables in Excel or understanding general statistical concepts to defend against agendas inside and outside their company.
Your data is measuring the wrong demographic: Your analytic methods and conclusions could be right on, but the problem could be that you started at the wrong place. Check to see if the customers you sampled are representative of who your marketing campaign is currently targeting. The good news about this issue is that most of the strategic work is already done. Once you fix your inputs, the new, improved conclusions should lead to more profitable results.
You’re comparing your campaign results to the wrong demographic: You may have the correct starting place, but your metrics are using the wrong comparison techniques to analyze how your customers are going to spend money in the future. If you’re working within a closed system (comparing buyers too narrowly or with inappropriate cohorts), bad comparison practices can lead to bad conclusions. For this problem, do some data diagnostics to divide your sample into more relevant subgroups. With the right subgroups, you might find out that your original strategy wasn’t going to work for everyone you thought it was. This is a great opportunity to try some more targeted nuances to get more value out of your marketing efforts.
Your customer base has simply moved on: One of the saddest problems for an analytic product is that the historical data and the conclusions you would draw from analyzing it is not related to future consumer decisions. Something else could have happened in the market or some new trend has caught the attention of your usual customers. So if your marketing campaign is built on historical trends that are not likely to reoccur in the future, you’ll probably just need to get creative and try something new.
We hope that this discussion about common analysis problems can help you and your team get your data mining efforts back on the railway to the insight goldmine! Leave us a comment if there are any other issues that your team has faced.