For enterprise companies, data can be a double-edged sword. On one hand, well-curated and organized data has the potential to unlock great insights, from unexpected customer behaviors to missed market opportunities. On the other hand, poorly handled data can mislead and distract—a problem that only grows with the more data you have at your disposal.
The right data analytics strategy is key to harnessing the power of data without getting bogged down by it. Enterprises should demand more from their data, and they can do just that by employing demand analytics, a lesser known but integral part of the data revolution. Far from simply being a strategy for approaching data, demand analytics has been proven to drive measurable financial results for businesses.
What are demand analytics?
Who drives the demand for your products and services?
Answering this question is essential to understanding demand analytics. And the answer, regardless of your industry or location, is the same: It’s your customer.
Demand analytics is the practice of gleaning important data insights in order to make business decisions that affect the demand of your product or service. More specifically, demand analytics blends big data and digitization with a customer-centric mindset to enhance the demand-side drivers of your business.
Why demand analytics drive real value
In PwC’s “The demand analytics premium; Getting the most out of your data,” James Walker and Joerg Niessing tell us that we can’t predict success on data analytics alone. A body of experience has emerged demonstrating a link between the mastery of demand analytics and overall business performance in all geographies and industries
Included in this same paper is a study that examined 500 executives and senior managers, who were each interviewed about their business’ analytics capabilities. Those with higher demand analytics capabilities performed better commercially. Of these more successful businesses, 70 percent invested considerably in their demand analytics capabilities.
It’s important to know these companies didn’t fit any one mold. Interviewees worked in 12 different industry sectors and were based around the world. Annual revenue ranged from $50 million to over $20 billion.
In other words, any company can benefit from demand analytics—the challenge is implementing the right tools in the right way to make demand analytics work for your company. Fortunately, this isn’t a harrowing journey you have to undertake alone.
Demand analytics in practice
Every day, Antuit helps global retail, CPG and manufacturing companies use demand analytics to predict, shape and meet demand to deliver measurable financial results. Let’s take a look at a few case studies in which we’ve already helped companies harness demand analytics to boost growth.
Case Study #1: Understanding Shopping Behavior for a Supermarket Chain
One client, a regional supermarket chain, faced a real problem. Big box retailers were driving down prices in key markets. Our client needed to find a way to remain competitive. Lowering prices across the board wasn’t an option.
To address this challenge Antuit turned to the company’s transaction and loyalty data. We applied predictive analytics within our weekly ad circular optimization tool to identify key purchasing behaviors, such as visit frequency, basket sizes, price elasticity and promotion responsiveness.
Our solution included the following:
- Price elasticity and demand curves to better understand shopping behavior
- Seasonality and pricing information for Known Value Items
- An optimization engine that calculated optimal product mix, price points and ad placements based on financial performance
- An intuitive user interface that allowed planners to enter business rules and constraints, and view results.
From the insights provided by these tools, we optimized the regional chain’s weekly ad circular and identified 8% margin increase opportunities. Most importantly, our client successfully defended its market position.
Case Study #2: Boosting Customer Loyalty for a Luxury Brand
Another client, a Singapore-based e-commerce luxury retailer, hit a wall. The company, which sells high-end brands, enjoyed regional success and had grown business operations across 8 nearby countries. Yet the company was having trouble earning repeat business.
We designed and deployed a marketing analytics framework and predictive model to identify members with the highest purchasing probability and provided insights into the right marketing channels to target these members. The company was also able to truly measure the lift of their promotional campaigns, helping them better understand which efforts generated the best results.
The result was an improved marketing strategy that drew customers back to the company. The first market to go live in Singapore saw improvements on marketing ROI in the range of 5–20% across their portfolio.
Case Study #3: Adjusting a Pharmaceutical Supply Chain
Our third example centers around a global pharmaceutical company. The company wanted to maintain the success of its top-performing product, isotretinoin, despite its lengthy cycle time from raw material procurement to finished product. Management decided adjustments to the supply chain were needed and wanted to evaluate the costs and returns on available options.
To quantify these options, we developed models that analyzed strategic supply chain design and inventory optimization. From these models, we identified a series of adjustments that could save the company nearly $2 million USD. The company wanted to increase its return on isotretinoin. Antuit showed them it was possible.
Solving the demand equation
The data revolution is multifaceted and nuanced. There are many ways to approach the understandings and insights data can provide us. Demand analytics shouldn’t be overlooked.
We’ve witnessed firsthand how demand analytics improves business performance in all industries and geographies. Its adoption may soon differentiate the companies that thrive from those that perish. Our clients’ challenges and needs may differ, but the results are always the same: demand analytics yields profitable growth.