How Retail Analytics 2.0 is Changing the Future of the Retail Industry

How Retail Analytics 2.0 is Changing the Future of the Retail Industry.png

With extensively fast-growing market and competition in the retail market, it has become imperative to satisfy the customer expectations along with optimising the serving business process. Hence, it has become crucial to manage and channelise the data to work towards customer delight and generate healthy profits to survive prosperously.

To achieve this, the big retail players globally, are applying retail data analytics at every stage of the retail process from tracking the favourite emerging products, optimising product placements and offers via customer heat to forecasting sales and future demand through predictive simulation.

Ideally, a retailer’s customer data gives you a report of the company’s success in reaching customers based on their past purchase behaviour, defining the best way to approach them through targeted marketing efforts and nurturing them and finally working out what to sell them next.

Using metrics such as conversion rate, average order value, recency of purchase and total amount spent in recent transactions, retailers built reports summarising the customer behaviour is providing them with general insights into the behavioural tendencies of customers.

But unfortunately, general behaviour tendencies being just too broad, these reports don’t provide the useful insights needed to define how individual customers are likely to behave. It takes more than a compiled report to create a meaningful conversation with customers that honours the shopper’s favoured level and mode of engagement.

This is where retail analytics provide the opportunity to change the retail marketing industry significantly. There are a few key impact areas where retail players see an immediate use as far as retail analytics is concerned.

Impact Areas

1. Forecasting Trends

Using advanced tools, retailers today can know the trends in the industry. Marketing departments can take advantage of the trend forecasting algorithm to decide what needs to be promoted and what not.

2. Predicting Demand

Retailers can focus on the sectors where there will be high demand for the profound insights they get from the buying trends of the customers.To build a picture of purchase behaviour across the target market, the retailer needs to gather information that involves demographic, seasonal, occasions led data and economic indicators which helps in inventory management in a better way.

3. Identify the Highest ROI Opportunities

To model anticipated responses in marketing campaigns, as measured by a propensity to buy / likely to buy, the retailers use predictive risk filters and data-driven intelligence based on the understanding of their current and potential customer base.

4. Predicting Future Performance

There can be a significant impact on existing or potential relationships based on each customer interaction. Before rolling out a new idea to the entire sales, risk endeavouring needs to be well monitored as it may lead to immediate as well as prolonged loss of profit.

Instead, by using a test-and-learn approach – trying an idea with some reps or customers and comparing the performance of the ‘test’ group to the performance of a well-matched ‘control’ group can help the retailers to isolate the cause-and-effect relationship between any strategic shift and critical performance indicators.

5. Price Optimization

Retail Analytics plays a vital role in determining the price of any commodity. Algorithm track demand, competitor activities, inventory levels, automated response to market changes in real time and actions to be taken based on insights in a time-saving manner which helps in determining the trend and helps you decide when prices should be dropped. Before the age of analytics, most retailers would reduce the cost during the end of the season when the demand was almost gone.

6. Creation of Client Profiles

Retail Analytics segments buyer’s data to create demarcating faceless mass into slots, personality points, through studying their purchases. Retailers enhance their retail data, by comb through transaction reports and loyalty plans to act on it.

7. Accommodating the Small-Scale Retailers

Retail Analytics data is also at the helm of small-scale retailers, who can take advantage of different platforms those are providing the services. Also, some start-up organisations offer social analytics to induct the product onto social media networks. Thus, small-scale businesses can bask in the merits of retail analysis without stretching their budget into bankruptcy.

But, while this may sound really impressive, retail analytics comes with its own set of challenges.

Challenges

Many issues need to be optimised to take the full advantage of the capabilities of retail analytics. Security, privacy, intellectual property and liability policies need to be more robust regarding retail analysis. Retail analysis encapsulates high-end analytics and companies would have to integrate information from multiple data sources, often from third parties, as well as deploy a dynamic data to aid such an environment.

Many times, companies fall in short-sightedness, failing to implement insights from analytics which can be fixed with continuous alterations of retail styles where a specific team is allotted for the task of an arrangement of insights and their implementation.

In a real-world example, consider a retailer that would like to appropriately message high-valued, loyal customers who appear to be disengaging from the brand. Retail analysis model built from stored data could identify which customers are likely to purchase again for seven days, allowing the retailer to let them be the loyal customers they genuinely are.

Retail Analysis can also show if satisfied customers are unlikely to purchase within seven days but have a high average order value. For these customers, the retailer could provide an incentive to bring them back to the brand. In either case, predicting what customers are likely to do is critical to understanding how best to complete the dialogue with them.

Conclusion

Retailing is the platform for more data-driven disruption and retailers will need retail analytics augment for marketing decisions because the quality of data available from internet purchases, social-network conversations, and recently, location-specific smartphone interactions have emerged into a new entity for digital-based transactions.

Retail organisations can reap benefits like improved performance, better risk management, and the ability to unearth insights that would otherwise remain hidden setting the retailers up for success as we move forward into the retail analytics.

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