RESEARCH REPORT

In brief

In brief

  • Only a few years ago companies were collecting data about their customers through consumer panels and face-to-face surveys.
  • Today, there are many sources of on– and offline data available, each containing more information than most organizations ever thought possible.
  • When joined with internal data, the potential business value this "new data" can create is limitless. Read on to find out why.


In our data-driven world, everyone leaves a trail. "New data" is the digital dust that consumers and businesses create—and that niche data technologies collect. And for today’s organizations, this new data could be opening up opportunities to connect to customers on a hyper-personal level, capturing their attention at the right time and place with the right message.

Rajat Mawkin, senior principal at Accenture, explains the Zero Moment of Truth (ZMOT), how businesses can utilize new data to interact with customers in these moments, and why the opportunity to increase customer value can be growing for both B2B and B2C companies.

Three years ago, it was predicted that by 2019, about 75 percent of analytics solutions would incorporate 10 or more exogenous data sources from second-party partners or third-party providers. In your experience, has this prediction come true?

A paradigm shift has happened. The availability of external data about consumers is exploding—and it might become more cost-efficient to acquire.

Due to the computing power available, we also now could have the ability to run multiple algorithms over this data so that we can connect all these disparate data sources. This means we now might have the ability to connect all dots together for an individual, like a name to an IP address to a device ID to the type of apps a person is using. So yes, this prediction might coming true.

What is the value of data services to today’s companies? And what impact could it possibly have on the customer experience?

Put simply, data services can help companies connect with their customers at the Zero Moment of Truth (ZMOT).

As soon as customers start searching for a specific product, they can provide multiple signals out of their search. These signals can be derived from their online browsing history, visits or apps on their mobile phones. Mining through these signals, data services providers can identify and segment customers and deliver targeted offer ads to them.

The timeliness of actions is very important during this period, as customers may not remain in this ZMOT state for a long time. Companies that know a person is looking for a product but don’t do anything for weeks, might risk losing this potential customer for life.

While the ZMOT usually occurs when the customer is at the very beginning of the decision journey—as they are starting to explore options through research and may or may not be aware of your products or brand—the first moment of truth comes when the customer purchases a product. The second moment of truth follows, when they use and experience the product.

The experience of a product is highly important, as it might either lead to a repeat purchase or result in a renewed search, thereby creating the next ZMOT.

The key is to target the customer at the zero moment of truth, the point when a customer considers their purchase through research.

Which sectors are paving the way in this approach to customer interaction? Could you give an example?

From experience, the banking and financial services sectors are further ahead of the rest, followed very closely by retail, largely because the data science industry within these sectors is a lot more mature. Their desire to understand customers and what they are doing when they are not transacting with them is one of the main reasons I think banks, insurance carriers and other financial services providers and retailers have such advanced data analytics capabilities.

For example, say a bank had a customer that wanted to attend a skydiving event in New Zealand—that customer would begin by searching for hotels, flights and related activities in the area. Their online browsing data could then send a signal to their bank; if the bank is able to derive insights from this data, they could reach out with a recommended personal loan to help the customer in their upcoming trip.

Along with the loan, the bank could offer the customer a concierge service to help them plan their vacation, or a travel insurance and a Forex card with no foreign transaction fees. And if this information reached the customer when they were at the very beginning of their decision journey, the bank might win them over for this and any other future trips.

How about B2B organizations—are they also able to utilize new data to better understand and connect to their customers?

Data services are still extremely useful in a business context—one of the main differences between these services for B2C and B2B organizations, is related to the types of data they take into account and the insights that are derived.

For example, a B2C business (as in the banking example above) may require data services to focus on online browsing history, social media activity, and wealth and financial data to derive customer insights. In the case of a B2B enterprise on the other hand, data services may need to draw insights from firmographics, online presence, growth trends, credit and business health information, and technology stack data.

Take a platform provider for example, whose main source of revenue is advertising for other businesses. To understand who would want to advertise on their platform, the company would need to have a 360-degree view of the market—the types of businesses, their online presence, their target customer base, their online spend and the type of key words under which they appear in search.

A data service provider would be able to give this organization the type of information they are looking for, and even make predictions about the type of customers that might be looking to advertise through its platform.

What are the potential benefits that come from allying with an established data services provider?

Using complex algorithms and leveraging the power of AI/ML, data services providers can consolidate the signals a customer is sending to predict the likelihood of a customer purchasing a product or service. They can then use these insights as a lead to be shared across a number of different organizations—giving more robust oversight to customer behaviors and actions.

For example, a customer might be looking to buy a car. At the beginning of their journey they might download a few mobile applications, search for cars online, look into auto loans and insurance, and visit local dealerships to test drive cars. At that stage, the customer is in the market for not only a car, but for insurance and a loan too.

This data—mobile, browsing and geolocation data—can be used by a data service provider to send companies in the car market, as well as banks and insurers, a number of signals so that they can reach out to the potential customer at that ZMOT.

Using complex algorithms and leveraging the power of AI/ML, data services providers can consolidate the signals a customer is sending to predict the likelihood of a customer purchasing a product or service.

What are the biggest barriers to deriving data from these new—and varied—sources?

There are three core challenges businesses need to be aware of if they decide to go down this path. The first is to identify the right data source for the problem your business wants to solve. The data services space is expanding quickly, which means that what the data providers are sharing with you could be similar or even the same, or at different levels of granularity. Some of the data may be name and address level, while other data could be cookie IDs, device IDs and IP addresses. This could make it all the more challenging to connect all this data together so that you can have a holistic view, or even DNA of your customers. It is advisable to select the data source based on the length and breadth of the data, completeness and accuracy, and volume.

The second challenge is to analyze all of this data together. Even if you are able to overcome the first hurdle, you may find you have a data set with data for around 100 million+ people and 20,000+ things that you know about each and every one of them. The question then becomes: How do I find exactly what I need to solve my business problem?

Last—but certainly not least important—is conducting due diligence and legal review on data. Companies need to vet all vendors they work with on an individualized basis, which might take a lot of time and may hamper the whole process. One way to speed this up could be to ally with a data service provider that already has established relationships with vendors, where this expanding data ecosystem has been thoroughly investigated and the necessary legal checks have already been carried out.

For organizations looking to apply data services and unlock this new potential, where is the suitable place to begin?

Defining the business use case is very important. Companies should not just acquire data because everybody else is doing so: If they don’t have a specific use case, once the data lands in their lap, they may not be able to use it at all.

Nail down the use case first and this could help to identify the right data to solve it. Finding the right data services ally is the next step. This is especially important for safeguarding against any potential privacy issues.

With the external data ecosystem expanding rapidly, there are many data vendors, data aggregators and data sellers in the market that might be supplying the same type of data. There are a few steps which may help you to choose the right data vendor, to suit your needs and your business:

  1. Data intake and use case assessment: Gather details on the data elements being used, data flow on the platform and final usage of the data. Your legal team should also assess the use of this data for the defined set of use cases.
  2. Data vendor assessment: Assess each vendor on a suite of parameters such as their data collection methodology, data security, data completeness, and volumes and customer consent to use data for specific purposes.
  3. Vendor contracting: These terms can help you to verify the correct usage of the data, a list of use cases and the data licensing term with details around its treatment post use (e.g., deleting the data after its use).

The companies that have a clear business objective and the right data partner by their side will be the ones that should derive the most value from new data—for their business and their customers.

Learn more about Accenture’s Applied Intelligence services

Rajat Mawkin

Senior Principal – Applied Intelligence

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