Stephen Budd and Vicky Brock of Clear Returns talk with IBM on retail analytics, big data and how insights into returns data leads to big uplift in retail profits.
IBM is a multinational and consulting corporation from the United States of America, with headquarters in locations such as New York. IBM manufactures and markets computer hardware and software, and offers services in hosting and consultancy.
The video can be found below from our official Clear Returns Youtube channel:
A recent study of more than 300 retailers released by SAP revealed that predictive analytics is almost universally seen as a competitive necessity in retail. At the same time, retailers noted that the pressure to keep up with big data has created a strain on employee knowledge and resources.
So what can retailer’s do? Installing analytics software can help, but that creates the additional task of training employees to interpret and apply predictive modelling tools to the business. This means costly and time consuming training in a highly specialist area, at the expense of these employee’s usual responsibilities.
Experts interviewed by Retail Times suggest that the increased need for predictive analytics expertise may cause businesses to selectively hire employees with knowledge of statistics and predictive analytics software and techniques. However, a shortage of specialists in this field makes this solution difficult to implement in the short term, plus these individuals may not have the necessary retail knowledge to interpret data in the most relevant manner.
Alternatively, the next step is to make use of new breeds of analytics software that turn big data into big insights, or more importantly actionable insights. Innovation in this space has led to more cloud-based, accessible tools which combine the advantages of big data with convenience, extracting the most important and relevant information - such as Clear Returns’ software.
This is one area to watch in 2014, as more retailers will continue to adopt new solutions to help them interpret their data faster and in more meaningful, actionable ways.
The first in a series of blogs from our ‘Data Ninja’, here we discuss outliers within datasets.
An outlier is a piece of data which falls far outside the typically expected variation. It’s easy to simply view outliers as a nuisance, since they can cause problems when you attempt to create models or visualise the data.
However, outliers can reveal all kinds of useful intelligence if you understand how to study them. Here are some steps for turning your outliers from annoyances into information gold mines:
1) Check for errors. Remember, it’s always possible that there’s a mistake in the record, so look for any evidence that the information was logged or processed incorrectly.
2) Think about what the outlier means in context. You aren’t just looking numbers on a a spreadsheet—it’s information about real patterns in things like shopper behaviour or product performance. Think about what a high or low number really means.
3) Gather additional information. See if the outlier is associated with any other unusual patterns in the data. Sometimes you may have to look outside the data set—for example; events such as extreme weather might have an effect on delivery timing.
If you can find out the reason for your outlier—or at least make an informed guess—you have gathered important information about your business that you might have missed by simply focusing on the average.
Posted by Data Ninja
Several articles published recently explore the key trends within retail technology that are set to shape the industry, here we deliver their key findings.
A recent article featured in the Wall Street Journal discussed the key trends in online fashion that are set to continue. Gartner and Retail Info Systems (RIS) also published their 23rd Annual Retail Technology Study, detailing the key areas of future investment for the industry over the next 24 months. The key findings show that online data and analytics along with mobile developments are the drivers set to shape the future of the industry.
Gartner and RIS surveyed c-level executives across the retail industry to determine their major action items for the next 2 years. They concluded that the top technologies for 2013 include:
1) Campaign analysis and forecasting
2) Standard forecasting and planning
3) Mobile POS
4) Predictive Analytics
5) In-store pickup and return on online items
The clear emphasis here is on analysis and forecasting, with 46% of retailers planning to upgrade their analytics and business intelligence technologies within the next 2 years. Although this will prove tough for retailers as 36% of executives surveyed felt that managing big data was one of their top challenges.
Only around 10% of retailers have a fully functioning mobile offering in place
Mobile channel development was another key topic within the study, the majority of retailers were in the process of planning their strategy while only around 10% had a fully functioning mobile offering in place. This will prove to be another challenge for retailers in 2013 as IMRG have just this week reported in their Quarterly Benchmarking Index thatmobile accounted for 20% of all UK e-retail sales in the first quarter of this year, a 5% rise from last quarter.
Merchandisers and buyers will become much more analytical in their approach thanks to these technologies
The study also looked at merchandising specifically. High levels of future investment are expected in this area on campaign analysis and forecasting and multi-channel forecasting. The article from the Wall Street Journal mentioned earlier stated that merchandisers and buyers will become more analytical in their approach. While Peerius’ latest Online Merchandising Index revealed that merchandising leaders ‘can still improve a lot’. Therefore implementing this kind of technology will only enhance these employees capabilities and their impact on the overall business.
Big data and analytics are the buzzwords of the industry right now. It’s never been easier to collect vast amounts of valuable data from a vast array of devices and touchpoints – but how do retailers make the most of these kinds of technologies?
As retail becomes increasingly complex and competitive through the presence of ‘omni-channel’ and higher and higher demands from customers it has never been more crucial to collect and analyse your data. Michael Ross of eCommeracommented in an article published today that for almost every decision retailers are required to make there is data available to inform them.
It has never been more crucial to collect and analyse your data
Although big data can’t just be big – it needs to be smart. A computer program can quickly spot significant connections between data variables but these connections might not be meaningful. For retailers, understanding and interpreting the significant connections in a huge dataset can be daunting – especially when skills in this area are in great need of development.
To help retailers deal with this enormous task there has been an explosion in technology innovation in this sector. Yet Michael Ross of eCommera stated in the same article that simple business intelligence reporting is no longer enough – decision intelligence is what is really required.
At Clear Returns we would take this one stage further, forecasting decision-making using big data. Neil Ashworth, CEO of Collect+, stated in a recent article by NACSOnline that retailers currently have access to hindsight and not real insight.
Retailers should be looking for solutions based on their business challenges that will allow them to make real-time decisions
Retailers should be looking for solutions based on their business challenges that will allow them to make efficient and profitable real-time decisions. John Lewis is an excellent example of a retailer who has achieved exactly that, the article by NACSOnline details a project the company took on giving 30 British technology companies the opportunity to build solutions for their key business challenges – which became so successful that they are now making it an annual competition!