In a world teeming with large and diverse sets of data, the ongoing challenge for many organizations is to manage this data effectively. Without the right framework in place, siloed data could mean missing out on the opportunities that proper data management can create.
The Problem With Disparate Data
A leading cause of disparate data is the broad range of devices and applications used per household. Cisco predicted that the average number of networked devices per person in North America would reach 13 connected devices by 2021. From smart speakers and home assistants to connected cars and cities, the Internet of Things is revolutionizing the world of work, rest, and play.
The common challenge in all these data sources is that they are represented at different levels of density. This creates layers of complexity when attempting to merge data streams and draw conclusions with high levels of accuracy.
Making Sense of the Madness
Solving for the above is possible only in a platform environment. The first step is to build models for data collection and cleansing, along with data science engines which can draw inferences from data. Once this process is established, Machine Learning can start to build dynamic customer profiles that reflect interests, preferences, and even a prediction of future needs.
Let's take a look at a common consumer scenario to see how consumer data unification and enrichment can reveal valuable data trends and insights.
A customer visits Sephora for the first time. She makes a purchase and opens a loyalty account at checkout. She also subscribed to the store's mailing list earlier in the week. Sephora now has two records to create her customer profile. But Sephora still has a lot of unknowns about the customer that could help better serve this customer:
- How far does she travel to get to the store?
- Which stores has the customer visited recently?
- What do these stores reveal about the customer?
- How affluent is the customer?
- Is she worth heavy marketing dollars?
- What interests and preferences does the customer exhibit outside the store?
- Is the product pitch speaking directly to her daily needs?
Near’s data shows that Sephora customers in New York:
- Prefer burgers from White Castle (41%) over Burger King (27%)
- Bank with HSBC (21%), followed by Bank of America (17%) and Citibank (13%)
- Pick up their coffee at X Caffe
- Get their gas at stations from BP (39%), Getty (12%), Mobil (12%) and Sunoco (8%)
By getting access to insights such as the ones above, a personalized customer journey can be built for each customer, improving her customer experience and boosting sales.
And this formula works regardless of the industry or the size of the organization. From airline passengers to auto dealership visitors, customer data enrichment provides an insightful view of the customer that enables brands to deliver personalized and relevant experiences.
Furthermore, when large enterprises are targeting a particular audience, data can point them towards the right moments to get their attention based on their real-world interests.
Rising Above the Noise
Many organizations are facing the challenge of scaling up their data management infrastructure to manage the variety and volume of data available today.
Combined with a data management process, investing in data management technology can help organizations treat their data as a corporate asset and use it to create the perfect the customer journey.
Also published in MediaPost