Tons of data is produced and captured by retail organizations, daily! However, it is quite unfortunate that often retailers fail to put all this data to proper use. It usually gets trapped within the silos, thus deriving value from it, becomes an impossible task.
Retail business has never been through such a drastic and tectonic shift. Purchasing patterns, preferences and expectation of modern-day consumers have not remained same. This change has made it imperative for the retailers and brands to adapt agile and dynamic data processing solutions. It enables the retailers to respond quickly and provide improved services to modern consumers.
Retail stores, both offline and online, not only gather massive amount of structured information, but even incredible amount of random information.
Sometimes, critical and valuable information about revenues, conversion rates & footfalls and occupancy gets shadowed under the random data. It therefore is not be used to fetch actionable and valuable insights about the prevalent trends and current state of the retail business.
Evaluating the situation on various axes like store, product, geography, landlords, etc. becomes a cumbersome task. This apparently disturbs the well-informed decision capabilities, adversely affecting the future strategies. Lack of a centralized data-bank and fragmented data makes it difficult to evaluate performance of both the stores and landlord. Moreover; when there is no system to analyze or visualize data on multiple dimensions, decision-making capabilities suffers severe set-back.
Therefore, in order to make optimum utilization of their data, the retailers have started consolidating data together within multiple databases and source systems. Their software solution providers, using this information developed solutions enabling retailers to create an impromptu report on the data available from varied sources.
Generally, data processing specialists create easily consumable reports using the details about sales, lease and store. Further, the data pulled from various heterogeneous sources is applied to robust BI tools to visualize the data and even perform descriptive analysis to develop predictive models.
Additionally, backed by advanced BI technologies and data processing solutions, retailers unlock:
Store Data Analysis:
- Demographic Store Data Analysis (based on particular region, city, state, country)
- Anchor store and vicinity impact Analysis
- Sales revenue Analysis by year and store
- Lease expense analysis to minimize capital expenditure
- Performance analysis consisting data related to store profitability based on various dimensions.
Revenue Analysis & Prediction:
Existing sales, lease and store data can be augmented along with demographic data. Using this, a predictive model can be created which helps in predicting revenues per square foot for any proposed store with utmost accuracy. Based on this predictive data retail chain can prioritize opening of new stores.
Traffic/Footfall and Conversion analysis:
For retail stores, improving footfall and conversion rate is of utmost importance. Data processing solutions gather BI by categories like state, city, store, over time. It even enables one to learn more about the effectiveness of marketing and rise in the conversion rates or footfall.
When we are discussing all about retailers, why forget real estate industry:
Real Estate Analytics:
- Revenue Vs Real Estate correlation and trending
- New Store opportunities
- Lease decision optimization
- Lease negotiation assistance
- Existing real estate portfolio rationalization
Besides, the above listed insights, retailers even get a better perspective about the following.
- Impact of weather and demography on Store sales. For example, colder regions have more online sales than physical store sales.
- There is a strong co-relation between online and store sales.
- In-Store customer experience and marketing strategy can help you to lure more customers to physical stores
With such actionable insights, the retail chain can plan to expand their analytics roadmap and move on to semantic analytics like social feed & sentiment analysis.
A data-centric approach can enable retailers to slice and dice corporate data from various source and show information in easily presentable formats like charts and dashboards. This further empowers businesses with speedy and better decision-making capabilities and predicts revenues of proposed new stores.