Why predictive analytics is the future of the retail economy

First, data is more accessible. Technology constraints in the past – including the high cost of storage – meant that collecting and preserving data was once a lot more time consuming and expensive. These constraints are lessening.

Second, data is more plentiful. Whether it is coming at you through social media, customer interactions, marketing campaigns, IoT devices, or smart devices, there is no shortage of channels through which you can receive a steady stream of high-quality data.

Third, data integration tools are more powerful. Data transformation and cleansing tools can create single-source-of-truth data for each customer. Once data is integrated in this way, it is much easier to make sense of for downstream use.

However, while enterprises have made new advances in data acquisition and processing, there is still a missing link: how do you align analytics with business goals? In fact, many organizations do not have a clear business outcome strategy or what they hope to achieve through data analytics. A common problem is that the data set in an enterprise data lake can be many times larger than that of a competitor, but if you can't make sense of the data and use it to achieve the right results, you have an isolated set of data.

Retail is a good example. In such an ever-changing industry, predictive analytics is critical. If you want to launch a new food product, how do you know if it will sell well in a certain region? Looking at the north and south of the UK alone, there are already many differences: southern England's consumer base is relatively young, thanks to the cultural and employment opportunities offered by London. But as you move north, the portrait becomes more diverse.

With retail predictive analytics you can make informed choices based on many scenarios:

• What would shoppers do if we launched a new product while discontinuing another one?

• What would happen if I created more choice by adding an imported product to one where a domestic provider may be faltering?

• How would competitive pricing differ for products based on ethical sourcing, and what is the upper limit consumers would be willing to pay?

With predictive analytics in retail, it’s possible to conduct many analyses using the historical data based on buying patterns and shopping behavior. For example, with your own customers you could create a predictive strategy to understand:

• how your competition is using data in their omnichannel strategies

• how seasonal uptake may influence your supply chain needs

• how shifting demographics may impact sales for different products

• which of your channels are performing well, and which are shrinking

• how different sales strategies might increase basket size

• how you can use real-time inventory and stock updates to make your supply chain more resilient

If you are a data leader and find yourself being promoted to a higher level decision-making role, what steps can you take to drive the growth and adoption of predictive analytics? Here are five strategies Sudhir suggests:

Honestly assess the maturity and adoption of data strategy in your organization

Does a robust data strategy even exist? If there is one, where does the team stand from a maturity perspective? Do they have a track record of success in predictive modeling? If that is the case, how can you ensure continuous innovation? If maturity isn’t high, what are the key enablers you should put in place to take them through the journey?

PS: If you're not sure how to perform an evaluation, consider using the Tableau Blurprint evaluation tool, or email dkminfo@dkmeco.com for further information!

Align data to business outcomes

biggest challenge any data leader faces is organizational culture. Everyone, wherever they stand in the corporate ladder, needs to understand the value of data. And that data value realization must be persona-based. To deliver on the vision, you need to have cross-functional, high-performing teams who constantly look into models and the data so you can make sense of it.

Take a data life cycle view

Too often, data still sits in disconnected silos and different formats. In addition to collecting data, you must enforce a practice of cleansing, maintaining it, and harmonizing it to turn it into actionable insights.

Build toward key success factors

By building predictive modeling, machine learning, and data mining tools, we can extract insights from collected data and use it for applications like future trend analysis and retail product demand forecasting. Creating a vision of what these tools can help your organization to attain in becoming more customer-centric makes them a much easier sell at the senior executive level.

Measure outcomes and revisit goals

With consistent data practices, you can define KPIs to measure impact and revisit your outcomes regularly. If your strategy is not paying off, or if you want to change direction, you can realign based on results.

Finally, whether you're doing predictive analytics in retail or any other industry, remember: the final step in making predictive analytics a success is advocacy. As a data leader, you must lead the team to collect data every time you interact with a customer's business, identify the right technology stack to achieve your goals, and build a data-driven culture. Only by making good use of data and realizing intelligent application of data can enterprises stand out and gain advantages in today's retail industry!

Over the years, Yueida has continued to hold online sharing meetings for enterprise applications and online live broadcasting of innovative technology products, sharing cutting-edge data dry goods and insights with many data experts rich in industry practical experience, helping enterprises and users to speed up digital transformation, and promoting the development of data literacy and data culture

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