How to use data to anticipate consumer behavior [Q&A]

Businesses are starting to recognize the advantages of leveraging data-driven approaches to improve customer experience. These approaches are also hugely valuable when included in strategic roadmaps to increase engagement and return on investment.

Analytical data is ideal for businesses wanting to assess the needs of their target customers to deliver personalized experiences and attain more accurate forecasting and demand planning. Moreover, data from analytics promotes more effective inventory and supply chain management, compounding supply and fulfillment issues businesses are currently experiencing

George Ioannou, managing partner for global experience design agency Foolproof, a Zensar company, talks to us about how businesses can use data effectively to help them anticipate consumer behavior and improve ROI.

BN: What tools and resources do businesses need to begin leveraging data?

GI: The essential ingredient to inform design, whether that's to shape a new product proposition through to making measurable product enhancements, is data triangulation: bringing together multiple data sources to build a rich evidence-based picture about target customers. Having this detailed picture of the target customer and their relationship with the product will increase confidence in decision-making for organizations building products. Data not only convinces us we are building the right thing, but it also helps validate the development efforts and business objectives.

To leverage data the most important resource a company has is its people. It's important they have the time and expertise to not just produce reports, but to step back to look at the wider product, the service and the ecosystem in which it sits.

Our advice to organizations is to spend budget and time on becoming data literate. In turn this will help people pick the right tools, know how to read, work with, communicate and debate with data across the business.

To identify the right tools needed to leverage data, ask a few simple questions to help guide your choices:

  • What problem is the tool helping you solve?
  • How quickly can you implement the code from the tool into what exists already?
  • How 'easy' is it to make changes to the code?
  • How does data governance work from a constancy and GDPR perspective?
  • Does this tool align with other tools and the existing enterprise architecture?
  • Could the tool solve cross-departmental challenges from the get-go?
  • What type of insight can data provide a business?

There are four levels of insight that we typically think of:

  • Descriptive (what happened?) i.e. 43 percent of people clicked on the latest offers call to action during the month of February.
  • Diagnostic (why did this happen?) i.e. I thought it was a good offer, it was nearly a friend’s birthday.
  • Predictive (what might happen in the future?) i.e. The offer is predicted to drive a click-through-rate of 50 percent.
  • Prescriptive (what should we do next?) i.e. Issue the customer who converted with this offer a free delivery on their next order; potentially driving 50 percent repeat purchases.
  • How can businesses use this insight to predict behavior and improve customer experience?

Insight can shape a product, service or experience as well as evolve existing propositions. However, if no one is taking qualitative or quantitative data seriously it's hard to move towards predicting behavior and evolving customer experiences to cater for it. Both forms of data must be in a symbiotic relationship to deliver insight that can be used to predict behavior and evolve the customer experience. They are also needed to measure the ongoing impact of their use. This insight needs to reflect the lived experiences of people and therefore data needs to be fresh, current and real to be adapted.

Data literacy empowers organizations to leverage data in a way that can go towards predicting behavior and improving customer experience. The baseline is the simple ability to understand and objectively comprehend what data is put in front of you. As this scales and an organization builds its predictive power those people can now work together more effectively by leveraging insight. With this understanding they can drive its usage, and work to move the needle on the different goals and objectives for customer experience.

BN: How does customer experience lead to improved ROI?

GI: Enhancing customer experience typically helps to improve return on investment by offering better experiences on websites, applications and with the internal tools and systems that a company might offer their employees. However, customer experience, driven by data, can only flourish where there is a genuine openness to receiving information in this way.

Using data to inform target metrics is also beneficial when improving return on investment. They are typically defined by a combination of business strategy and the customer behaviors you're hoping to drive. These measurements can help to; improve prioritization, promote ideation and keep the business on track by focusing on specific metrics.

BN: Can you give us an example of a business using data to its advantage and seeing ROI?

GI: Cydar Medical uses information from patients to inform their algorithms for mapping endovascular systems to help doctors make better decisions about surgery while improving patient outcomes. In addition, they also use the power of situational observational research to inform how the system is evolved, demonstrating a symbiotic relationship between qualitative and quantitative data and how it can be used to improve technology and design.

BN: Are there any challenges to analyzing data that need to be considered?

GI: Analysis can sometimes uncover insights that sound convincing but that are based on faulty logic, either as the result of errors in reasoning or poor-quality data sets. This can come from cognitive bias and predictable mental errors that arise from our limited ability to process information objectively. We commonly encounter confirmation bias, where people look for data that supports their beliefs while rejecting data that goes against their narrative or vision for the evolution of a product or experience.

Good analysis takes time. A disregard for the time needed causes poor outcomes. You have stakeholders who request the data rather than insights as insights take more time to understand and process. This often takes the form of 'I want to know how many people clicked X' rather than providing the analysts with their business problem so they can offer genuine insight into the problem space. As a direct result we see bad decisions being made based on an incomplete view.

Another challenge is business silos and a lack of clear leadership around data -- where is its seat at the table? Everyone wants to use data but nobody really wants to champion it. As we know, sometimes the answer is not in one place, it's often spread across multiple tools with different owners and teams using them. If there was one thing every company could do today it would be to agree on someone with seniority and autonomy who can champion customer data insights.

Image credit: ml12nan/

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