Data is an invaluable currency for most companies these days. In 2015, users generated nearly 8 zettabytes of data worldwide — and that number is likely to reach 35 zettabytes within just a few years. Companies are contributing to that massive statistic with basics like customer names and contact information, as well as new demographics such as purchase histories, travel patterns, social engagement, behaviors, attitudes and much more.
This bounty of data — and the options for collecting and analyzing it — often tempts companies to enter a feeding frenzy of data acquisition and analysis. On top of that, the time-sensitive nature of many types of data adds to the sense of urgency.
Points like daily or weekly sales results are critical for a brand and its stock ranking, urging companies to shuttle data quickly out to various stakeholders. Because email is typically the cheapest form of communication, the attitude is often to simply “send to everyone,” a wide-field approach that is quick, simple to deploy and inexpensive.
But is this approach actually effective?
Not always. Rushing to leverage the data-age without proper planning is riddled with dangers. As with any endeavor in which details are key, haste is not your friend. While speed is often an advantage in the marketplace, when it comes to data, accuracy is just as important — if not more so. Rushing data out to everyone in the organization without taking the time to check its integrity is a disaster waiting to happen.
The Dangers of Rushing Data Through the Pipeline
How can botching data accuracy affect a company’s mission?
One issue is customer satisfaction. When data is misrouted for customer requests, it results in poor personalization and reduced engagement at best — or even the alienation of customers. Just as restaurants shouldn’t forget to hold the mayo, data-serving companies must prioritize getting data request orders right.
Insights from data allow companies to craft precise messaging for their customers based on individual needs and preferences.
This can be done as a form of “storytelling,” a scalable approach that can deliver timely, measurable and action-oriented messages to one’s base — but only if each customer’s relevant details are understood. These details can include spending behavior, buying journey and other salient information.
Understanding these details requires data that’s both accurate and effectively managed. To see why, consider how different categories of products or services inform the messaging that ought to be sent to a customer. Depending on the category, what drives customer decisions could be the branding, price, convenience or quickness of service.
By using the appropriate data, companies can better understand both the category and the consumer’s intention across the purchasing journey.
This allows marketers to more easily influence customer decisions with relevant offers and product services that are delivered at just the right time. But mishandling this data may result in a poorly targeted marketing attempt, which can put off customers rather than entice them into a purchase or engagement with a brand.
Data Done Wrong
Mismanaged data can lead to blunders, but when things go well, it can be a powerful connector for customer and company.
For example, how does a brand stand out within the highly regulated, sensitive healthcare sector? One pain point in this sphere is customers’ challenge to acquire medical insurance approvals — an area well-suited for data-based assistance. Identifying patient differences and supporting each customer individually with personalized reports and targeted information helps them on a path toward engagement and purchase.
Of course, missing such opportunities isn’t the only harm that mismanaged data can cause. Poor use of data insights can cause an increase in opt-outs — or even prompt bad reviews and unfavorable chatter on social networks. What begins as a rush to dive into the exciting data age can quickly turn into an unintentionally self-destructive marketing campaign.
Navigating Data Pitfalls
With so many potential dangers attributed to mismanaged data, how can companies work to avoid the trouble spots and create a winning data strategy? Here are four steps to ensure data flows are effective, allowing analysts to glean the most valuable insights:
1. Use expectations to your advantage. Customer data is generated by customer interactions. So channel those interactions effectively from the start to encourage an inflow of relevant, actionable data. Of course, every product sector will be different, and customers will be engaged by different things, so prioritizing is key. You can do so through customer discovery and transparency, such as providing the provenance of food or clothing.
Consumer control can be prioritized by empowering customers, like Waitrose does in its “pick your own offers” initiative. And the community can be emphasized by connecting consumers within your area — something the London government has employed to great effect with its recent “Boris Bikes” bike-sharing scheme.
2. Use data to pull the right value levers. Data should always be seen as a tool for providing relevance and impact in your communications. Factors like purchase motivators, where a customer is in the buying arc at any given moment, and other relevant details enable brands to send the right message at the right time.
3. Use privacy by design. This is critical for building trust. Enabling customers to see what data they are sharing and modify their consent easily, along with enabling the logging of complaints, fosters security and retention of customer control — all vital to helping your customers feel like their voices and concerns are being heard.
4. Use foundational data techniques. These may include data audit and discovery, effective and ongoing modeling, and test and learn. Oftentimes, staffers are untrained in deriving value from data, so it is essential to call in specialists for training.
This can touch on not only marketing, but also legal aspects, recognizing data relevance and its role in engaging customer interest, and other issues.
Foundational data techniques are not only critical for marketing, but they may also help address any legal considerations that might come up during data-mining, as well as data’s role in engaging customer interest.
Keep in mind that it’s not just any data that matters to business success; it’s accurate data that generates timely, well-targeting messaging. With this, companies can effectively compete for customer advocacy and profits in the data age.
Do you have any tips to share on how you avoided bad data? What data collection tools do you use to plan for your marketing campaigns?