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The Essential Guide to Omnichannel Analytics

Omnichannel analytics is a method to analyze data gathered from various channels of a retail businessstore, like offline and online, to help develop a holistic persona of the customer, and other use cases. 

In a world where over 66% of the world’s total population is connected to the internet, today’s consumers expect a seamless shopping experience, both offline and online. 

Leading to the rise of omnichannel retail. A marketing strategy that provides customers with a seamless and cohesive experience across all channels.

This rapid adoption of omnichannel retail is the reason behind the rising importance of understanding omnichannel analytics. 

This blog serves as your essential guide to omnichannel analytics, covering everything from the basics to its significant impact on the retail business.

What Is Omnichannel Analytics?

To understand omnichannel analytics, we should first learn about omnichannel retail. 

Omnichannel retail is the natural evolution of businesses in the retail sector. In omnichannel retail, the business interacts with the customer through every touchpoint of the buying process, both online and offline. 

Hence the term, omnichannel, or multichannel retail. 

Omnichannel analytics is therefore a process of collecting and analyzing the data drawn from the various sources in the omnichannel funnel to generate actionable business insights. 

Omnichannel analytics empowers the retailer to accurately track the customer through their journey from their starting point to the after-sales interaction. 

Processing offline and online data generated by their stores enables retailers to accurately highlight the pain points of the customer. 

Broadly put, there are three reasons why a retailer should consider adopting omnichannel analytics.

  • Enhancing intra-store inter-department communication
  • Comprehensive map of the customer journey
  • Design tailored customer experiences 

Ways Omnichannel Analytics Help Retailers

Research by Google’s CEE and IPSOS threw up some interesting highlights. They surveyed over 4,200 consumers using four different types of products, laptops, TVs, mobile contracts, and clothes. 

Key learnings from the research 

  1. 80% of consumers research before purchasing a product 
  2. The average number of touchpoints for all four products was 2.8 
  3. Most customers search for product reviews, compare prices, etc before purchasing 
  4. 50% of consumers researched product reviews online while at the store 

Better Customer Engagement

Leveraging the insights derived from omnichannel analytics enables retailers to comprehensively understand their target customers. 

Retailers can use this data to tailor their products to meet the requirements of their customers. Furthermore, omnichannel analytics help retailers personalize their customer experience. 

For example, the retailer can integrate results derived from omnichannel analytics to build targeted recommendations and identify the most effective channel to promote their product. 

Inventory Prediction 

Stocking the correct location with the exact product is what sets retailers apart. Omnichannel analytics enables retailers to accurately predict the exact products to stock by monitoring the interest shown by customers on their website or application. 

This allows the retailer to stock the exact number of products without over-purchasing and registering a loss. 

Leveraging omnichannel analytics to only place orders that will sell, the retailer can reduce losses stemming from storage, shipping, handling, etc. 

An added advantage for retailers who leverage results from omnichannel analytics is that there would not be a requirement to sell the excess inventory at a discount, thus incurring a loss. 

Effect of Omnichannel Analytics on Marketing

The impact of omnichannel analytics on the marketing of retailers cannot be understated. 

This report by Deloitte, states that 56% of every single dollar spent on marketing is influenced by omnichannel avenues. 

This statistic is undeniable proof of the ability of omnichannel marketing to influence the success of a product or service. Furthermore, data derived from the online activities of the target audience can help create targeted marketing campaigns.

Moreover, leveraging omnichannel analytics in a retail business helps the retailer identify the channels yielding the highest return on investment. Thus, ensuring that the retailer invests in solutions that give the best return on investment. 

How Omnichannel Analytics Facilitates Collaboration

In omnichannel analytics, data from various sales channels are combined to develop a comprehensive picture of the customer. Resulting in teams that might have otherwise not interacted to share their insights. 

Furthermore, this allows retailers to develop quick responses to changes in the market. 

Omnichannel Analytics Helps D2C Retailers Go Offline

A recent report in the Economic Times highlights the prevailing trend of direct-to-consumer brands starting physical stores. Brands like MamaEarth, and Nykaa, are amongst the many digitally native brands who are opening new stores and expanding their physical footprint. 

But different from their traditional physical competitors, these digitally native brands adopt omnichannel marketing strategies to improve their revenue and reach and wider audience, giving them an intrinsic advantage over their competition. 

But, to effectively capitalize on the advantages offered by omnichannel retail, the D2C brand has to open its physical store. A process whose difficulty increases due to the prevalence of the gut-based instincts of the field team. 

Several online-first D2C brands have employed the capability of GeoIQ which developed custom-built AI solutions to help the brand identify the perfect location for expansion. 

How to Implement Omnichannel Analytics

The first step in integrating omnichannel analytics into a retail brand is to gather diverse data sources into a single repository. 

This would involve gathering the various sources of data, converting them into a single uniform structure, and then storing them where they can be easily accessed. 

This process is also known as Extract, Transform, and Load (ETL).

Once the retail store develops the framework for the ETL, they can then add various other sources to the repository to help develop actionable insights. 

Following the implementation of the data repository, the retailer can focus on identifying the metrics they want to track and develop the required dashboard. 

From analyzing the data, the retailer will be able to develop a picture of the movement of their customer. 

This will include,

  1. The touch point where the customer entered the system.
  2. Their various touch points up till the sale. 
  3. Their post-sale activities 

Assigning values to each touch point allows retail teams to anticipate the buying intent of the customer and adopt strategies to improve conversion. 

Conclusion

Omnichannel analytics provides retailers with the right tools to understand and enhance the customer journey by integrating and analyzing data across all touchpoints, online and offline. 

Incorporating omnichannel analytics reduces data silos and forces diverse teams to interact and work with each other to develop a holistic and personalized shopping experience. 

Omnichannel analytics helps improve customer experience, helps predict inventory levels, and helps marketing teams design effective campaigns. 

The adoption of AI in retail has reduced the learning curve involved in developing a robust omnichannel analytics method. 

Similarly, the adoption of AI has also reduced the challenges faced by digitally native D2C brands in identifying profitable locations for expansion. 

Presenting RetailIQ 

When a D2C brand considers expanding its offline presence they do not have a wealth of pre-existing data to choose from owing to the prevalence of the unorganized sector in Indian retail. 

Leveraging the inferences provided by an AI solution like RetailIQ empowers D2C brands to accurately identify their Total Addressable Market, identify the attributes determining the characteristics of the target group, estimate revenue, ascertain the trade area of the store, and spend quotient of the target audience. 

RetailIQ is a cutting-edge AI solution revolutionizing offline expansion for the retail sector. It is built on top of extensive location data and brand data to give street-level answers to expansion problems. 

RetailIQ is a powerful tool providing site recommendations to maximize success at the store level and minimize the risk of closure. It also helps identify the total addressable market, conduct competition analysis, better understand your target audience traits and their presence, and perform detailed site analysis and reports, all adding up to a successful expansion strategy. 

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