Recently, marketing strategies have been shifting towards one-to-one engagements. Offering a personalized customer experience has proven to be quite effective in nurturing relationships with customers. Because of this, large brands are making huge investments in order to improve the functional efficiency of their marketing operations. With the vast amount of data being collected from multiple channels, brands must elevate their marketing efforts beyond mass mailings.
Most brands are aware of the fact that they have to adapt data-driven marketing strategies, but many of them have not figured out how to operationalize the data in their databases. The data they have is incomplete, broken and difficult to utilize in marketing operations.
Let’s take a look at the most common data challenges marketers face:
1. Tendency to Expand Data Sources
In last decade, marketers have been focused on gathering more and more data. Their data sources were limited and all available data was used to evaluate customer behavior. But, as the number of data sources increases, data is becoming vast, confusing, and even contradictory. Despite this, many marketers still tend to focus on expanding their data sources and gathering more information.
Transforming this the data into actionable insights takes time and requires a finesse many data scientists lack. Therefor,, marketers should focus on utilizing their marketing budget on technology that can track and consolidate their data, and provide actionable insights.
2. Outlining the Customer Journey
Many times, marketing operations executes a sales pitch either too early or too late. It is essential for a marketer to have an understanding of the timeline of the customer’s journey; to grasp how they move from brand awareness to conversion. Since touch points are spread across multiple channels, it is hard to synchronize online and offline brand-awareness data. There is limited to no continuity, and information is often repetitive.
It is hard to integrate online and offline data because people consume information from such a wide variety of sources. Someone might come in contact with a brand on a billboard, visit the website from an office desktop, and make a purchase on their tablet. But, according to a survey, less than 30% of retailers are collecting data at the POS stage, and a mere 34% are tracking offline purchases. Using big data correctly is equally as important as gathering it. In order for that to happen, marketers need to dedicate their budgets to tracking offline sales and filling in the gaps within their online data.
3. Ambiguity over Right Technology
To ensure data is well organized and comprehensive, marketers need to be well aware of what technology best fits their needs. Marketers have to choose between having an in-house resource or delegating the decision process to third party agencies.
The majority of marketers use inaccurate and fragmented ways of visualizing their customer experience. The issue is that every segment, be it retail, high-tech, or CPG, has a different set of needs in this arena. Along with this, data sources vary significantly depending upon the size of the data and various demographical attributes. This means there is no ‘one-size-fits-all’ solution for data management. Any technology a company invests in must align with their goals for data useage.
4. Deriving Actionable Insights
Once the above challenges have been taken care of, there is one final step before the data is useful. Let’s say we have chosen a suitable set of data sources, have a clear picture of the customer journey, and have opted for applicable technology. All of this effort and investment will be lost if we cannot draw insights which are actionable to get closer to our prospects.
Before making a big investment in technology and data collection, it is important to understand the results you can derive from your intended analytic tools. Also, make sure that the solution you are choosing has uninterrupted support, flexibility, and is adaptable for future enhancements. In the era of real-time data, sticking to one mode of data utility is not sustainable. Just like data, customer preferences change rapidly, Be prepared for that.