Introduction
The retail sector has undergone significant transformation over the past decade. The rise of eCommerce has forced traditional players in the segment to re-evaluate their investment priorities and direct a significant portion of their annual budgets to engineer newer digital capabilities for both consumer-facing and internal operations.
Among these digital aspirations, retail analytics is often a top contender for the lion’s share of IT budgets. Today, organizations can achieve a clear advantage over the competition if they are able to implement the right big data analytical solutions following best practices in data engineering, data science, and data visualization. This would enable them to set the foundation for newer disruptive innovations like AI/ML.
In 2022, an analysis found that the global market for retail analytics surpassed $6.59 billion. This figure is expected to rise to a staggering $23.53 billion by 2030.
Whether you are a small, medium, or large organization, if you are in the retail domain, data analytics is today an unavoidable component that should be at the heart of your digital stack. Let us have an in-depth look into retail data analytics solutions and uncover the top business benefits they bring to the table.
Why should retail organisations adopt retail data analytics?
Customer Centricity
The most significant benefit that data analytics solutions bring to the retail industry is the ability of businesses to understand and build a shopping experience that puts customer interests in the driving seat. Every customer touch point leaves a data footprint that can give a lot of clues into what they love, how they shop, and what makes them love or hate a retail touch point. Making sense of this data can help retailers erase the friction that most customers complain about in their shopping journey.
Data analytics solutions can help a retailer predict the exact area of a store to place a particular merchandise so that it grabs maximum eyeballs from store visitors, which can ultimately lead to more sales in the long term. They calculate this based on data like distance walked in the store, proximity to stations like in-store café, distance from the entrance or billing counters, etc.
Eventually, this data can be monetized by the retailer by striking deals with retail brands to place their products in areas that attract more patronage and sure-shot sales. This makes it a win-win for both the customer and the business.
Price Management
Retailers base their strategic discussions mostly on their ability to realize revenue from inventory. A critical component of sales is striking the right balance between profitability and pricing of products that are up for sale across digital or physical outlets.
Data analytics strategy can help retailers arrive at a sustainable pricing model for different goods by analyzing data on past purchases, product shelf-life, logistical efforts, margins, and the ability of the product to initiate future up-sell or cross-sell activities.
For example, an analytics solution can help a retailer fix a lower price on a printer by helping them bundle a periodic subscription for ink refilling along with the initial sale. This ensures that the lower upfront price will help win the sale and assure the retailer of a continuous revenue stream for the refills.
The exact price can be determined by processing data such as the cost of ink, inflation metrics over the subscription period, the potential demand for refills, and much more.
Forecast Accuracy
We have seen how events like the COVID-19 pandemic threw all forms of logistical and supply chain infrastructure into utter chaos. In times like these, retailers need to be equipped with advanced knowledge on how to instruct their suppliers, vendors, sourcing partners, warehouses, etc., to ensure a seamless and optimized inventory. This is where data analytics can play a critical role.
By processing how long it takes for each component in the logistics or supply chain to fulfill demands, analytics lets retailers forecast their inventory replenishment times more accurately.
By adding demand metrics from the consumer side into the picture, it helps give a clear picture of what retailers must do to ensure that the demands of essential products in a given season are fulfilled on time.
Analytics-driven insights direct the collaborative effort needed in the backend. Forecasting accuracy helps not just the retailer but also their partners, vendors, etc., to optimize their operations by knowing in advance their workloads for an upcoming period.
Intelligent Marketing Personalization
Studies have found that over 70% of customers are left frustrated when their shopping journey is not personalized. This is a wake-up call for retailers to ensure that they put the data collected from customers to good use in all areas possible.
From marketing campaigns to cross-sell and up-sell recommendations, every customer demands a hyper-personalized experience in every outreach program. This may include aspects like language localization in campaigns, consideration of budgets or behavior for shopping recommendations, contact or medium of shopping preferences, etc.
Data analytics helps retailers uncover these deep-rooted personalization traits which their customers love. Just like how shopping journeys are built with customer-centricity, data analytics can help retailers nurture relationships with existing and potential customers by engaging them in ways they cherish after the first contact is established.
Personalization efforts can target a group of customers having similar traits as well. This allows retailers to gauge better returns in the form of sales in shorter time spans.
The Pratiti Advantage
The business benefits of retail data analytics are too good an opportunity for retailers to ignore. This is especially true in the wake of rising competition and dominance from eCommerce-only players.
The challenge here is to pick the right analytics solution and map the most fundamental data metrics needed to solve your business challenges into the analytical decision engine. This is where an experienced partner like Pratiti can be a game-changer.
Our range of services in data analytics provides a 360-degree picture of how your retail business can leverage the best ROI from data lying idle across digital touchpoints. Our experts can help you in preparing the right roadmap to success by establishing a combination of data analytics capabilities like
Descriptive data analytics – know what is happening.
Diagnostic data analytics – get to know why something is happening.
Predictive data analytics – uncover what is going to happen.
Prescriptive data analytics – learn what your business needs to do to stay afloat.
Get in touch with us to know more.
After successful stint in a corporate role, Nitin is back to what he enjoys most – conceptualizing new software solutions to solve business problems. Nitin is a postgraduate from IIT, Mumbai, India and in his 24 years of career, has played key roles in building a desktop as well as enterprise solutions right from idealization to launch which are adopted by many Fortune 500 companies. As a Founder member of Pratiti Technologies, he is committed to applying his management learning as well as the passion for building new solutions to realize your innovation with certainty.
