3 steps to improve your consumer experience with Big Data


The evolving digital behaviors of today’s consumers have reshaped the strategies leveraged by agencies and their advertisers to stand out from the noise in competitive marketplaces.

Traditional consumer loyalty factors, such as price and product quality, have taken a back seat to the overall consumer experience. However, many organizations have struggled to provide a top-notch consumer experience that is cohesive, fast, and customized due to an ever-expanding multi-channel, multi-screen world. In fact, according to eMarketer, only 7% of marketing professionals feel that their company has the ability to deliver real-time experiences to their customers. This is due in part to the lack of data collection and the methods employed to leverage that data once it has been collected.

Big Data, which can be defined by volume, velocity, and variety, should be the cornerstone of your decision-making process when it comes to developing your consumer experience. Leveraging data that gives you a full understanding of your target audience’s entire digital footprint, enables you to glean deeper, more holistic consumer insights and create advanced consumer profiles. Backed with this knowledge, you can build effective consumer experiences that compel prospective customers to convert.

In order to take full advantage of Big Data and create a compelling, effective consumer experience, there are three steps you need to take:

1. Break the data silos

Using multifarious data sources enables you to build a comprehensive consumers profile. However, this can be challenging to manage depending on the various technology stacks existing within your IT infrastructure. Also, the nature of the data, whether it is internal or external, structured or unstructured, personal or impersonal, etc. may create silos which impede you from yielding useful insights.

To overcome these challenges you must map your customer’s journey across multiple touchpoints, consolidating your 1st-, 2nd- and 3rd-party datasets within one platform such as a DMP (Data Management Platform). Then, using a DSP or similar technology, you can tap into the data and reach your prospects and customers across all platforms and devices with relevant content that aligns to their specific needs at that point in their journey.

2. Use predictive and optimization models

In order to leverage the aforementioned assets, improve your performance, and gain a competitive advantage, analytics models can be highly useful. These models help in identifying or confirming new opportunities and foreseeing the potential business outcomes.

To utilize these models, you should start by data mining, or identifying patterns (statistically significant correlations) from a pre-existing database. But, before you begin, you should establish a hypothesis, challenge your biases, expose your blind spots, conduct experiments, refine, iterate the process, and choose the less complex model to match the company’s capabilities. If you do not start with a specific goal, your efforts will be wasted.

3. Organizational transformation

Building relevant models requires both skillful data scientists and the right assumptions which reflect the strategy of the company. It means that you will need to convene meetings with different cross-functional colleagues who also know the business needs.

Moreover, taking and applying model-based decisions across the organization is not an easy undertaking because it may suffer a lack of credibility if not aligned with your employees’ targets; or not understood at all. This is why planning, training, and coaching sessions is an essential condition. Only a well-trained workforce can leverage it with the help of a clear blueprint, and a simple, user-friendly interface to visualize how it can lead to results. Moving from a data architecture to an information architecture.

Benefits of Big Data for the consumer experience

Once you have a handle on your data, you will gain a holistic view of your customer’s entire journey. From consolidating all consumers’ attributes across multiple sources, to better grasp their needs and provide bespoke offers, cross-sell and upsell with the right products on the right channels at the right time; to identifying the impediments they encounter along the way: streamline inefficient processes, address their requests faster, and increase customer satisfaction and retention.