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Did you know that most Marketing Managers say that they are committed to data-driven marketing, despite not being ready for it at all? The reason for this is not lack of knowledge or initiative, but poor-quality data. Marketing based on incorrect or old data is simply doomed to fail. In this article we highlight the biggest drawbacks of marketing with poor-quality Master Data. Don’t forget to download our infographic containing lots of useful tips.
A 23-fold increase in new customers, annual growth of 30%, 70% higher customer loyalty – these are just some of the benefits enjoyed by data-driven companies. Data plays a key role, particularly in market development. It’s very frustrating when you can no longer reach a business partner by phone or when mail keeps coming back undelivered. And that’s just the tip of the iceberg. When it comes to segmenting a market or targeting potential customers, bad data is a real spoilsport.
So, what exactly happens if your marketing is based on data that isn’t kept up-to-date? We’ve put together a list of 9 disadvantages for you.
The classic: you send out 20,000 letters to customers, but 10% of them never reach their intended recipient. For every 1 dollar spent on postage, 2,000 dollars are lost, and it’s the same for every single mailing. If you’re sending out a mailing every 2 months, for 1,000 dollers you could just as easily hire a temp to clean up your data.
The situation is similar when contacting customers by phone. Although there are no charges, valuable time is lost. With every unsuccessful call, the salesperson’s frustration increases.
With digital communication, the problem of incorrect contact addresses is even more serious. A distribution list that contains a high percentage of obsolete addresses causes bounces, in other words error messages from the e-mail server. Major providers like GMX and Google measure bounce rates and penalise companies with high error rates. They place them on blacklists and categorise their mails as spam. As a result, mails are no longer delivered even to the correct e-mail addresses in your distribution list.
Marketing based on a “flying blind” approach – in other words without any data – is possible, but very expensive. If you don’t assign customers to segments, you end up targeting them all, which inevitably leads to scenarios such as single males receiving advertising for nappies, for example. This doesn’t go down well with consumers. Studies have shown that companies that frequently target potential customers with irrelevant advertising lose brand value.
Cosmetics giants have begun only showing advertising for sunscreen when the sun is shining at the website visitor’s location. This is not personal data (in the context of GDPR), but it is highly individual and relevant. This is a good example of how the right data – in this case the potential customer’s geographical location – boosts marketing.
Bad data, on the other hand, can end up annoying consumers. Have you ever seen online advertising for a product that you have already purchased? Displaying this type of advertising banner is known as Retargeting and can be very intrusive.
Amazon and Netflix have raised the bar when it comes to meeting customer requirements. Customers now expect an online store to provide functions that allow the user experience to be customised – either manually or automatically – to suit their specific requirements. At the most basic level, this involves providing users with recommendations based on their own purchase history or that of other users. Master and interaction data is merged and combined. Communication takes place at a personal level: “Frank, here’s a film that we chose for you because you’re a fan of horror.”
The same rule applies in the B2B sector. An individual offering targeted at the right person in the company is far more effective than a generic offering sent to the info@ address.
In today’s world, data analyses are an effective tool for identifying new markets, segments or customers. They take existing data and enrich it with external sources to gain useful insights. The best-known form of this is the statistical lookalike. This is a person or a company with the same characteristics as your most profitable customers. There is a strong probability that they too will become a customer, but only if you are able to identify and target them. However, you can only find lookalikes if your data is in order.
Customer Service probably suffers most directly as a result of bad data. Support agents require all information about a customer. Only then will they know what the product in question is and whether there is already a support history. There’s virtually nothing more annoying for a customer than having to say the same thing to three different support agents. And anything that irritates customers is potentially costly for the company. If Customer Service is not meticulous in recording all interactions, and if the Master Data is not clean and current, this is likely to lead to high costs. Customer satisfaction will, in all likelihood, also hit rock bottom.
Have you ever configured a car online? Leather seats? Yes, please! Premium sound system? Absolutely. Sports chassis? Eh, hello! Of course, this will only work if the Master Data is correct. It would be awkward if the manufacturer couldn’t deliver the selected configuration. This applies in both the B2C and B2B sectors.
There’s no question that correctly recording supplier and item numbers is essential for efficiency in purchasing. Of central importance here is the continuous updating of data to reflect all changes, for example when a supplier relocates. This works best when automated via API.
Things get even more interesting when you enrich your Master Data with additional information. By adding a financial indicator for each customer and prospect, you can identify bad deals before they even happen. This prevents unnecessary losses or having to chase up deliveries.