The apparel industry is the laggard in embracing advanced analytics, often favouring merchant and designer-driven ‘gut feel’ over insight-driven decision making. But with the possibility of bagging a 10-15% cost saving in operations and supply chain, can businesses continue to afford to ignore its benefits?
Today’s consumers are demanding quicker, end-to-end personalised offerings. But for the apparel industry, complexity has never been higher across assortments and channels. On top of this, there’s increased competition from nimble and new digital-native brands to contend with. So what’s the answer? Analytics. Being able to harness data to inform “core business decisions” can apparently turn even the most complex use cases into opportunities for growth, according to a report from McKinsey and Co.
While advanced analytics has long been used by other industries, the report – ‘Geek meets chic: Four actions to jump-start advanced analytics in apparel’ – reveals the apparel industry is the laggard in embracing analytics, “often favouring merchant- and designer-driven “gut feel” over insight-driven decision making.”
Among the reasons for its slow adoption are poor data quality, a rapidly changing assortment and competitive landscape, high SKU and logistics complexity, and limited analytics expertise within the current employee base.
But the apparel industry is missing a trick according to the report. When used in the right way, application of analytics can drive a 10-15% saving in operations and supply chain; a 50% reduction in high-performing employee churn; and a 15-25% improvement in spend effectiveness.
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“A number of successful apparel wholesalers and retailers have begun to crack the code on analytics and they are now seeing material benefits,” say the report’s authors, Rich Fox, Maura Graul and Althea Peng. “We regularly witness specific analytics applications increase top-line sales or improve the bottom line by 2-10%, with larger transformations achieving a more than tenfold return on investment.”
Here are four things apparel players need to consider when adopting advanced analytics for their businesses.
1: Not everything matters: Prioritising to win
First, decide where analytics will achieve the greatest business impact. Applying it everywhere can run the risk of it not reaching the scale or level of insight needed to create a competitive advantage. A cross-functional roadmap should be developed to make the tough decisions on where analytics matter most. Companies should scan all areas of the business to identify the largest advanced analytics opportunities, as well as those that are most critical to the organisation’s strategy.
Then, functional teams can build individual “use cases.” To be successful, use cases must be well-defined and specific. The best ones solve a particular business problem and achieve a measurable benefit by applying advanced analytics.
“Assortment optimisation,” for example, is far too broad a use case to be effective. Instead, teams should consider how they want to optimise the assortment – such as improving a mix of basics versus fashion, establishing ideal size curves by product category, or balancing price points. And also what the business impact will be, such as increasing availability of popular items to increase sales, or reducing excess inventory and markdowns. These can then form individual use cases.
Once use cases are identified, they need to be prioritised. Trying to pursue each of the advanced analytics use cases simultaneously is the fastest way to fail. Some use cases may have a massive financial impact but the largest opportunities are more complex and require significant time and investment. Other use cases are smaller in scale but can be implemented quickly. Ideal analytics roadmaps balance quick, impactful wins with longer-term, more complex investments.
2: It’s all about data: Extracting privileged insights
While for the most part valuable, data can sometimes be a trip-hazard for an apparel company with ever-changing assortments, rapidly changing trends and product attribute needs, and non-standardised SKU-naming processes across wholesale customers. Multiple facets can result in relatively poor data collection and quality on product attributes, customer behaviour, and supply chain history.
Building the appropriate data ecosystem should be a core piece of an advanced analytics journey. Data sources should be expansive, but prioritisation should be guided by target use cases.
Data can be enhanced within a single use case over time. For an apparel retailer looking to enhance its personalised messaging in customer relationship management campaigns, but one that lacks a comprehensive customer data platform, initial pilots can start with whatever data is available, such as recent shopping behaviour. Over time it can expand to include email engagement, online browsing behaviour, coupon usage, and demographics. Adding external data to get a comprehensive view of customer behaviour can push the insights further and better serve customers.
As additional data sources are layered in, new use cases can be introduced. Initial data might simply identify customers who are highly likely to shop. Over time, however, these models can be enhanced to determine what those customers will shop for, when they will shop, and how much they will buy.
3: Chase the value, not the calendar
At most apparel companies, the seasonal calendar is the dominant operating rhythm of the business. At any given time, the organisation is simultaneously selling one season, producing another, and designing for yet another. These long lead times too often expand beyond product development – innovation can become pegged to these seasons as well, and timelines for projects can extend over long periods due to the need to navigate calendar-driven capacity constraints.
Instead, using an agile operating model to pursue analytics use cases dramatically accelerates time to impact. Agile models use short, structured “sprints” to accelerate impact and keep business owners engaged.
An agile approach is emphasising output versus process: teams should feel autonomous enough to shift direction if something is not working. It emphasises progress over perfection: sprints are designed to produce prototypes, which can then be tested in real business settings as quickly as possible. Shifting to a more rapid operating model may be the hardest change to manage in this journey, but it will substantially accelerate the analytics roadmap.
4: Designers versus data scientists: Winning the talent battle
Apparel is a fundamentally art-based business that will always require creative direction to ensure that products remain innovative, relevant, and beautiful for the consumer. Investing in analytics and technical skill sets – for example, data scientists and architects, as well as coders and developers – will certainly be an important aspect of an advanced analytics transformation. But apparel players should place additional emphasis on finding and training analytics translators within their organisation.
Translators become the cross-functional glue that helps infuse analytics throughout the organisation. While translators will not be the ones building analytics models or data architecture, they will be critical for ensuring that analytics investments are translated to action, ultimately generating value for the business.
Apparel companies typically use a combination of internal skill-building and targeted recruiting to assemble a bench of analytics translators. When developing internal skills, many organisations have established translator curriculums to train their teams. We also often see apparel players develop translators within their planning and merchant functions, as these areas typically employ basic analytics in their day-to-day activities. By including these groups in the creation of the analytics roadmap from day one, apparel companies can both ensure the roadmap is aligned with business objectives and help to facilitate buy-in among their peers.
