With so many retailers out there, every morning our leaders from the retail industry wake up with the same question – where should I invest money to grow my business and be more profitable?
Few of the sure shot areas are:
- Invest in a new pricing strategy to better compete with other brands.
- Increase budgets for advertising and promotions.
- Refurbishing stores to make them more attracted to shoppers.
- Optimising the assortments to differentiate with the competitors.
- Re-engineering of the supply chain to improve on-shelf availability.
Choosing on that list and executing properly to deliver the highest return on investment has never been as tough it is nowadays with the emergence of a new generation of customers who are highly informed and more demanding.
The good news is that retailers now have so much data available to manage their business – be it data about their own store, or their competition or even the customers.
But, the bad news is that retailers yet don’t know what to do with the available data!
So where do we gather the data from?
The most basic and ready-to-use tool at your store is the POS system! If your point of sale system is only being used to ring up sales, you’re definitely missing out. Most modern POS solutions these days come with reporting features that can shed light on important metrics such as profit margins, basket sizes, customer counts, sales trends, and more.
If you’re keeping in touch with customers via email, be sure to track open rates, clicks, and times of engagement. This will allow you to optimize them better.
Tools like people counters and beacons can provide data such as customer counts and dwell times.
Data extraction from in-store surveillance cameras to scrutinize the behavior and shopping pattern of customers.
Trolling social media such as Facebook, Instagram, and blogs for product reviews could be essential in finding out peoples choices.
A dip into their own supply chain covering orders, shipment, and inventory to get customer data.
Modern consumers go through multiple channels on their path to purchase, so if you’re storing and analyzing their information in silos, you’re going to get fragmented profiles of your shoppers, and you could miss out on key insights and opportunities. Analyzing online and offline data together will give you the complete picture of your customers’ shopping journeys.
How to turn data into insights?
Next step is to turn the data into valuable insights! Retailers need to use business analytic tools to analyze data and extract actionable and relevant information.
After the SAS monopoly, there are a lot more options of open source and licensed analytics tools that accept raw data and give business specific reports to action on. Few open source tools are listed below:
- R – R is now the most popular analytics tool in the industry. It has surpassed SAS in usage and is now the tool of choice. R also integrates very well with many Big Data platforms which have contributed to its success.
- Python – Python has been a favorite of programmers for long. This is mainly because it’s easy to learn a language that is also quite fast.
- Apache Spark – Spark is another open source processing engine that is built with a focus on analytics, especially on unstructured data or huge volumes of data.
- Apache Storm – Storm is the Big Data tool of choice for moving data or when the data comes in as a continuous stream. Spark works on static data. Storm is ideal for real-time analytics or stream processing.
- PIG and HIVE – Pig and Hive are integral tools in the Hadoop ecosystem that reduce the complexity of writing MapReduce queries. Both these languages are like SQL (Hive more so than Pig). Most companies that work with Big Data and leverage the Hadoop platform use Pig and/or Hive.
Few licensed tools are listed below:
- SAS– SAS continues to be widely used in the industry. Some flexibility on pricing from the SAS Institute has helped its cause. SAS continues to be a robust, versatile and easy to learn tool.
- Tableau– Tableau is an easy to learn tool that does an effective job of slicing and dicing your data and creating great visualizations and dashboards.
- Excel – Excel is, of course, the most widely used analytics tool in the world. Non-analytics professionals will usually use Excel.
- QlikView – Qlikview is another data visualization giant. QlikView is supposed to be slightly faster than Tableau and gives experienced users a bit more flexibility.
- Splunk– Splunk is more popular than some of the more known names like Cloudera and Hortonworks. It started as a ‘Google for log files’ which means its primary use was to process machine log files data.
Choosing the right tool for your business depends on the company budget and the volume of data processing.
How to turn insights into profits?
Retail analytic tools will process data and give actionable reports and visuals for business to analyze. The company needs to establish a cross-functional core team focused on delivering quick wins that can take business decisions depending on the reports.
Retail analytics will help you manage the below effectively:
- Category management: helps to select the right items to put into the store with the right price positioning and the optimal promotion activity.
- Marketing effectiveness: helps to optimize the marketing spending and deliver the right message to the right people to ensure they will go and buy.
- Supply efficiency: helps investigate the root causes of out of stock and maximize on-shelf availability.
- Store optimization: helps in deciding where to open a new store and how to perform at store level to maximize growth.
- Loyalty programs: helps in maximizing sales depending on loyalty programs.
A lot of things are evolving with the changing retail landscape but fortunately, something remains unchanged! Retailers who best anticipate their customers’ wants and needs offering the right product in the right place at the right time and for the right price will prevail. Investing in BigData talent and system is a mindful investment a company can make.
The world’s most valuable resource is no longer oil, but data!