Big data seems to have the answer to all of your questions. Many companies are hoarding data, unsure of its significance, not knowing if there is any use for it or not. Almost everyone focuses on the volume, velocity and variety of the data. But another V might be of more or equal importance – Value. When “value” is taken into account, it can be assured that every data point will help the business understand the customer better. Making the other 3 Vs, volume, velocity, and variety, might give marketers misleading directions. Decisions based on faulty big data can range from temporary embarrassment to complete customer estrangement. Overconfidence in the quality of data can backfire at any time.
Some of the effects of not ensuring the quality of data collected can be
Asking for fast commitment
Micro-targeted messages based on big data have its share of pitfalls, even though they can be reasonably accurate. A well-known example of this is – father learning about his daughter’s pregnancy through a retailer’s offering.
Consumers are gradually getting used to this kind of personalized message, the way someone might react fully depends on the message and that individual. Studies suggest it is better to ask the customer’s permission before reaching out to them with highly personalized messages or promotions. If the information is self-disclosed, the reception is mostly positive.
Too much, too soon without any prior consent can come across as invasive and create distrust.
Sending the wrong message
Asking for fast commitment is not as bad as sending the wrong message.
Even though this is not a frequent occurrence, it is not unheard of. This can not only be a waste of marketing budget but can also cause a lasting negative impact. This type of reaction often causes an adverse reaction called the boomerang effect, pushing a customer from a neutral, nonexistent, or positive attitude to a lasting negative one.
Creating wrong user profiles
Clustering and creating user personas and profiles solely based on data gathered from 3rd party big-data might not create accurate user groups; putting people in the wrong group can lead to sending the wrong message or asking for early commitment. And targeting them causes waste of marketing budget and valued customers.
Predicting inaccurate outcomes
Creating consumer behavioural or any other prediction model based on flawed big data can result in misguided decision-making, causing organizations to focus and invest in the wrong direction. In one incident back in 2013, a search engine-based flu-tracking model wrongly forecasted an increase in doctors’ visits related to influenza, more than double what the Centers for Disease Control and Prevention (CDC) predicted.
So what can create bad big data?
- It can be outdated or incomplete information.
- Organizations using multiple data sources incorporate data sets incorrectly.
- Lack of effort to identify inconsistencies.
- Incorrect assumptions about consumers’ interests.
- Malicious parties might corrupt data.
- Using the wrong data source
To increase the likelihood of having accurate data, organizations can do the following.
Know where the data is coming from:
Knowing where the data are coming from can give organizations a clear understanding of what to expect. While learning the source of organizations’ data is a must, it is also essential to know the source of data received from data brokers. Not all brokers are comfortable with sharing their sources. As many licenses from each other as different brokers cater to different data use cases. Knowing the source of data gives the ability to see if they have adequate control over the data accuracy. In addition, understanding the procedures in place with these sources to track changes, measure accuracy, and ensure consistency can give insight into how reliable the data can be.
Explore the data sets
Before using any data from outside sources to make any decision, the data should be explored and analyzed at least a bit by the internal teams. Doing an exploratory data analysis or a simple cross-check with verified data can tell a lot about the quality of the data gathered. If verified data is not available, doing exploratory analysis checking against individual and industry information can be used to make sure the data makes sense.
Connect with customers
It is essential to measure the success of targeted marketing that uses big data campaigns, which can be done by directly asking the customer about their experience. Beyond quantitative or objective measures, creating feedback opportunities within micro-target groups can give very useful insight. After feedback collection, reviewing, incorporating, and updating strategies based on the feedback can make the whole process more efficient. Responding directly to customers providing feedback can usher in more useful feedback and create a more trusting relationship with the whole customer base, making them feel more valued.
Reward customers for their data. It is also worthwhile exploring customer willingness to update or correct the information available about them. In order to do so, giving them more direct value and helping them understand what they might get from providing the information can be the best approach. The benefits of having accurate customer data with an active direct line of communication with them are much more valuable than any amount of big data; a deeper connection with the customer can positively increase the lifetime value.
To be successful big data should be taken as a tool that will complement the rest of the instruments available in the toolkit. It is not possible to do everything with one tool, especially if the quality of the work is essential.
Social media and other tracking tools’ can give wildly different results from what the reality is. This is what some researchers call “big data hubris”. Organizations should never assume that big data can substitute for traditional data collection and analysis methods. Big data should be used alongside data gathered through traditional methods to get the best results and avoid disasters.