Author Bright Masih | Senior Account Manager Retail Sector | Core Compete
Date: 27th February 2018
Retailers are cracking the omni-channel transformation challenge with aplomb with shoppers now hopping between device, location and channel at will, much to the delight of heavily invested brands. In line with innovation, In the UK in 2017 mobile shopping rose to 49.7% as a share of all online retail spend, compared to 42.3% in 2016. Whilst this increase is promising for retail on the whole, retailers are painfully aware that the success is not an entire win, and in some cases a double-edged sword, especially in the context of e-commerce returns costs.
consumers are naturally taking advantage of generous shipping and returns policies designed to meet expectations and encourage repeat purchase. These carefully crafted policies are designed to tempt shoppers to visit more frequently with personalisation supporting conversions. Policies also inadvertently encourage intentional returns but satisfy consumers who expect convenience at every step. Intentional returns also have the undesired effect of slicing through profits designed to be delivered by expensive and meticulously curated Christmas ad campaigns as well as early and ever more aggressive sales discounting.
Intentional returns have costly repercussions for retailers, when you consider that on average a returned item touches seven different sets of hands before the item is finally restocked, often at a significant discount before being resalable. The cost of handling forward and associated reverse logistics is seriously hampering healthy trading results. Clear Returns estimates returns cost UK retailers an eye watering £60b last year, with £20b from items bought online with approximately one in three purchases returned.
Phenomena such as “Take Back Tuesday” isn’t helping. According to the Logistics Consultancy LCP an estimated £17bn worth of goods were bought online in the Christmas period between Black Friday and Boxing Day. As 15%-20% of the goods bought online are eventually returned, £2.5bn worth of goods would be refunded which doesn’t include the processing costs. Intentional and non-intentional returns are now part of the fabric of retailing with consumers frequently purchasing several SKU’s in a bid to find the right product.
According to Geekwire Amazon lost a staggering $7.2b in 2016 on reverse logistics alone, up £2m on 2015. Even though returns are a burden, retailers know that nothing can kill a relationship faster than unfairly charging for returns or making the returns journey cumbersome Despite this we have found that many retailers appear apathetic. According to UPS, 81% of customers want an easy returns process and over 66% of online consumers read the retailers returns policy before committing to any significant purchase so the returns journey and how they will be treated is clearly important to consumers.
Just as with the initial purchase, consumers expect to know where their purchase is and what stage it is at. Retailers on the whole have failed to invest in the customer the same way when it comes to returns with consumers often left wondering whether the return has been received and processed and critically when money will be refunded. It could be argued that consumers are being treated poorly despite that fact that future stickiness and repeat purchases are dependent upon their returns satisfaction.
According to UPS over half of online consumers are dissatisfied with the online returns processes they have experienced. This isn’t an unknown issue to retailers; however, many are falling short of the expectations placed upon them by ever more demanding consumers who simply expect convenience and communication. Consumers are never going to be happy with paying for returns and online retailers need to invest and innovate to reduce the net 30% returns average.
By ensuring the process is painless and simple retailers can delight consumers, a recipe for advocacy and repeat purchase. With the lifetime value of the customer at stake, in an ever more ruthless marketplace, todays retail leaders need to face the returns issue head and be decisive. Retailers must insure data scientists unpick returns data to establish trends, examine customer feedback across every channel, identify problem outliers and model returns processes in order to understand potential.
We have found that our retail clients are addressing the online returns issue by taking a two-pronged approach. Firstly, they are seeking to understand the root causes of the customer returns by analysing the massive amounts of transactional data and product reviews through in depth, cost-effective analytics through the solutions that we have implemented. For this we consult and recommend an analytics stack which delivers the right combination of processing power, elastic scalability and affordability.
Retailers are able to attribute what portion of the problem is caused by what issue and then set-out to improve problem areas such as online sizing, product displays, inaccurate descriptions etc. We have enabled retailers to understand the returns process as a step in the customer journey, and by correlating the pre-purchase browsing intent to the returns outcomes the implemented solution is able to provide actionable insights that are leading to significant operational improvements and reductions in reverse logistics processing cost.
Retailers are also experimenting with ways to drive traffic into stores by enabling quick and easy returns in order to benefit from impulse purchases. Through the analysis of the sales data of returns customers, one of our apparel customers decided to implement a concept where instead of waiting in a queue to return an item, a shopping assistant checks you in, let’s you browse the store, then comes to you when they are ready to process your return using a mobile device.
Through a carefully crafted test they determined that through this subtle change, they were able to gain 0.72 units for every return unit they processed. Devising effective strategies to address the problem and realise the opportunity requires a precise understanding of your customers, deciphering what they are telling you through their data, then making the returns customer journey a convenient one for the customer and a profitable one for you.
Recognised as an international leader in retail sector big data analytics consultation and service delivery explore our retail sector expertise to see how we have helped retailers stay ahead of their challenges. Visit us on stand B2916 at Big Data World on the 21st and 22nd March 2018 at the Excel Arena London to hear Travis Perkins, Shop Direct and Advance Auto Parts talk about their experiences as Core Compete retail sector clients.
Core Compete is Google Cloud Partner, Hortonworks Gold Partner, SAS Gold Partner, AWS Advanced Consulting Partner, AWS Managed Service Partner and an AWS Big Data Competency Partner.
Core Compete ranked 3rd fastest growing consulting firm in North America by Consulting Magazine
New York (Oct 22, 2017) – Core Compete, a global consulting firm that enables organizations to deliver value from Big Data, today announced that it has been ranked the 3nd fastest growing consulting firms in North America. Published by ALM’s Consulting Magazine, the list includes the top 100 fastest growing firms based on last 3 years’ revenue growth between 2013 and 2013.
Core Compete is a big data and analytical services organization that quickly delivers measurable financial value to enterprise clients through its Systems of Innovation and Agile Analytics Infrastructure offerings. Established in 2012, Core Compete is headquartered in Durham, NC with offices in Europe and India. Core Compete has SAS Gold Partner status with SAS Institute, the leading analytics software company in the world, is a Hortonworks Gold Partner, a Google Cloud Partner and an Amazon Web Services Partner.
“We are proud to be recognized for the second year in a row as being one of the fastest growing consulting firms in North America by Consulting Magazine. Our continued growth is a reflection of our customers’ confidence in our ability to help them accelerate, innovate and automate their big data initiatives,” said Shiva Kommareddi, Managing Partner of Core Compete LLC.
Core Compete has been providing innovative solutions to Retail, Manufacturing, Banking and Financial Services organizations in North America, UK, Europe and APAC.
To see the complete list of Fastest Growing Consulting Firms in North America, please click here.
Why IT Fumbles Analytics
By Donald A. Marchand and Joe Peppard
In their quest to extract insights from the massive amounts of data now available from internal and external sources, many companies are spending heavily on IT tools and hiring data scientists. Yet most are struggling to achieve a worthwhile return. That’s because they treat their big data and analytics projects the same way they treat all IT projects, not realizing that the two are completely different animals.
Accelerating Innovation by Adopting a Pace-Layered Application Strategy
Analyst(s): Yvonne Genovese
Gartner’s Pace-Layered Application Strategy is a methodology for categorizing, selecting, managing and governing applications to support business change, differentiation and innovation…
David is an investment banker based in New York. At least twice every month he visits Redwood hypermarket to shop for groceries and other personal needs. Three months ago, he married his girlfriend from China.
David was lounging around on a lazy Saturday when he received an SMS from Redwood. The message offered 40% discount on woman fashion brands at a partner retailer and 30% discount at famous Chinese restaurants in central New York.
After seeing the offer David was impressed with Redwood. He was also surprised as most of their earlier offers were bundled for grocery items. Why did Redwood think that these offers were relevant for him? He then smiled and thought that he will definitely avail both the offers next month on his wife’s birthday. He will gift his wife a lovely handbag and take her to one of the restaurants for dinner.
How did Redwood figure out this highly relevant offer for David?
David is a loyal customer of Redwood. Redwood Customer Loyalty team uses advanced analytics method to identify the customers’ life stage. They track the customers’ basket size, product mix and frequency of visit to segment customers into groups such as large family, family with kids, family without kids, Single, occasional visitor etc. Loyalty team observed that in recent months David’s monthly spend at Redwood store has increased and the basket composition has changed. They noticed that apart from larger baskets, transactions for his household now routinely include women’s beauty and hygiene products.
Redwood employs automated analytics where the system captures such changes in behavior, and identifies opportunities to enhance marketing around this life stage changes.
The change in the product-mix in David’s basket can be permanent or temporary (a friend or a relative visiting). Redwood loyalty team can test the hypothesis that this is infact a change in relationship status and that the partner likes Asian food. Redwood to test their hypothesis has created a special personalized offer for David and extended discounts at partner fashion retailer and restaurant chain. They also plan to send such multiple offers over the period of next two months. If David responds to offers regularly, Redwood will be able to conclude that David is married. This information can be used for more personalized offers.
Redwood has realized the power of data analytics and used it efficiently to enhance the relationship with the loyal customers.
Data volumes in your organization are exploding and your data warehouse is bursting at the seams. You have heard of all the hype about Hadoop but it is not well-suited for your workloads of real-time reporting. You don’t seem to have any other options other than to buy that dreaded expansion license (1 TB = $ 1 Million) for your data warehouse. Does this sound like your predicament? Read on, it’s worth at least $990,000.
For one of our customers utilizing the Core Compete A3 service, we identified a model where they could move a majority of the newer, transient work-loads onto a cloud based data warehouse, while utilizing their current data warehouse for traditional workloads. Leveraging Amazon’s Redshift data warehouse as a service, we proved to them that data from syndicated data providers can be made ready for analysis by analysts in 1-2 weeks cutting down the 3-6 month process that is currently employed by the standard data warehousing process. This significantly increased their ability to respond to demand signals when they launched a new drug.
The cost of the AWS Redshift solution, was less than $10,000/ Terabyte. In addition, this system was integrated into other services such as AWS S3, that allowed the customer to store a significantly larger volume of data in an environment that is a fraction of a traditional data warehouse cost (1/10000th), while being able to bring it online for analysis in a matter of minutes.
One of the early concerns that the customer had was around the security model of the cloud based solution and potential risks around this. However, through a series of reviews, we were able to convince that our AWS based architecture was designed to address the demanding security expectations of the client.
We integrated AWS Redshift into the client’s SAS and Tableau environments. The enterprise class SAS Environment which was behind the client’s firewall, was able to send SQL pass-through commands to Redshift, allowing us to leverage the MPP capabilities of AWS and bring back summarized data into SAS for further analysis. The Tableau server environment was deployed directly on top of the Redshift database to enable real-time access to the data for the dashboards that are now widely used to understand launch performance.
Now, the client is also engaged with us for a variety of pilots with integrated delivery networks (IDN), where they bring the IDN data into the Core Compete A3 Service to identify insights specific to the IDN to reduce re-admissions and increase Medicare related shared savings for the IDN.
How does your doctor make sure that you don’t get re-admitted? Generally, they are awash with data about your health, but have very limited data on your behavior outside the health system. On top of it, they really don’t know how to synthesize all this information in the limited time they have with their patients to come up with an actionable and tailored set of actions.
Forecast Health, a Durham, NC based start-up precisely aims to address this problem. Synthesizing data from Electronic Health Records (EHR), Insurance Claims and more than 100 consumer behavior oriented data sets, they provide actionable predictive risk scores that are integrated directly into the EHR system that the physician already uses.
While what they do is amazing in its own right. What’s even more amazing is that they were able to get from the ground up and running with a lean staff in less than 60 days. Leveraging Amazon Web Services, Core Compete built a HIPAA Compliant Cloud Environment that enabled the Forecast Health staff to bring large datasets from Center for Medicare Services (CMS), syndicated data providers that provided patient level identifiable data and from within the health system in a cost-effective manner. In addition, leveraging the elasticity of the AWS cloud and the variety of on-demand services such as EMR (Hadoop) and RedShift, Core Compete provided an environment that was designed to scale for the most highly demanded workloads. The entire environment was validated by external auditors.
“The Core Compete team enabled us to get up and running very quickly allowing us to prove our capabilities very quickly to both our investors and our early customers. Without Core Compete and the AWS cloud, our business would have never taken off”, said Dr. Michael Cousins, President, Forecast Health.
Steve was reading the newspaper and having his morning coffee. It was 20th November. He realized that after three months on the same day, he will be celebrating his 10th wedding anniversary. He remembered that he had promised to take his wife, Lisa, on an adventure trip to South Asia, to celebrate their 10th wedding anniversary.
He thought for a moment and said to himself “Better not to remind her, not sure if I can pull it off”.
Later that evening, he rushed home and said “Dear Lisa, we are going on adventure trip to South Asia to celebrate our 10th wedding anniversary”.
What happened in the 8-10 hours that Steve changed his decision?
Steve received an email from his bank stating that he could enjoy interest-free credit on purchases on his credit card for next six months and enjoy his wedding anniversary. Bank had also increased the line of credit for three months.
Steve thought he could avail this offer and pay it off when he gets his bonus in March. He’s been doing well at work and was expecting a nice bonus. But he wondered whether the offer being made in a timely manner was just a coincidence or something that the bank carefully orchestrated! Steve brought this up and wanted to know if it was possible that this was not a coincidence. I said “This is not coincidence but most likely based on the banks assessment of customer life-time value using the data generated by various banking relationships with customer. They also computed the risk score for their customers. These two numbers collectively portray the customers’ potential value and riskiness. They combined this with life events (such as a birthdays/ wedding anniversaries to make timely offers”.
“I can understand that bank maintains records such as date of birth, anniversaries etc but how do they know that I am expecting a good bonus?” Steve interrupted.
I smiled and explained “It is very easy, if the customers has salary account with the bank or if same bank is the primary bank for the customer. Banks are able to deploy customer decision hubs that bring all the customer’s data into one place, and are able to proactively trigger marketing activities leveraging a 360-degree view of the customer’s financial and life events.”
Steve chuckled and said “I am glad even the bank thinks I will get a good bonus, better remind the boss.”
My birthday is coming up and Kohl’s sent me an offer in my email: “Here’s a $10 gift for you to spend at Kohl’s to get your celebration started”
This really touched my heart and I was wondering why this meant so much to me (especially given that a Kohl’s coupon is not the hardest thing to get, there are in fact three others in my mail box right now with different offers).
Here are some reasons I came up with:
While I probably have atleast a dozen or more loyalty club memberships and many of them take the trouble of sending an email to say Happy Birthday, this is really the first time someone sent me a gift. While an email (that in my mind doesn’t cost anything) from an impersonal entity (AAdvantage wishes you a happy birthday!) was something that I was almost insulted by, as I knew it was a form letter, this communication from Kohl’s seems like they really meant it
It was a gift not an offer. They did not say 10% off, they did not say buy $50 and get $10 off your next purchase, they just said here’s a gift no obligations. This does not sound like a marketing offer but a gift from a friend
They reminded me to use it to get the celebration started with this. I was not thinking about the celebration earlier, but now I am thinking: What should I treat myself to at Kohl’s (that second pair of sneakers that I have been thinking of?)
I would have thought that in the crowded world of loyalty, I would be bombarded with such offers. Nope. Just one offer so far, and unlikely that I will get any more.
A few things that Kohl’s could have done better:
It arrived on the Sunday before my birthday, Saturday AM would have been better timing
It did not have any creative relevant to me (man vs woman at least)
Anyway nice job Kohl’s and I will expect great things from you.
Businesses across the globe are waking up to the need for putting their customers at the heart of every decision. The need to better channelize the innovation budget to address what customers really want, has never been more acute.
A PC giant with over $30BN in revenues, has been looking to incorporate customer-centricity deep into their product development and planning cycles. Traditionally very strong in the enterprise PC segment, they want to replicate and augment their success with the consumer PC segments – hence the need for customer-centricity and the call to Core Compete.
Disclaimer: We don’t have Sherlock Holmes and Dr. Watson on our payroll…yet!
Dr. Watson: Sherlock, how do we get to know what customers want?
Sherlock Holmes: Well the old school would say – ask them. The traditional way has been to leverage market research campaigns to interview relevant candidates and categorize their responses.
In a typical campaign, you interview existing or potential customers, trying to understand their preferences, likes and dislikes to landscape potential innovation areas for the next product cycle. Towards the end of the survey, you go about identifying the behavioral type of the interviewee so as to slot their responses into relevant customer segments. A good example is Conjoint Analysis where the questions are designed to evoke customer responses to potential product profiles and feature trade-offs.
These campaigns also involve “tracking studies” that comprise of periodically conducted surveys. These are typically shorter and are good for maintenance of the preference models created earlier, just like you would service your car.
Dr. Watson: So, what’s the catch?
Sherlock Holmes: Nothing, it’s awesome but don’t you want to be more awesome. It’s got a few glaring limitations that have intensified in today’s fast paced world:
Dependence on the sample quality: The preference scores thus generated from survey responses inherently contain the noise caused by low sample quality
Lack of volume: In today’s world where even an abacus would generate gigabytes of data, a marketing research sample can rarely be large enough to be conclusive on all the questions you have
Expensive: Scaling up the sample size of the research effort is expensive with costs increasing linearly for every additional response
Dr. Watson: So is this “old school” a passé then?
Sherlock Holmes: Not at all. I am apparently old school too, remember? Well, this technique has existed eternally for a reason and iterations over time have only improved it statistically and economically. Maybe there is a way to best use its most powerful offering, which is, getting exact responses for limited but targeted questions. Hold on to that thought and we will come back to it.
Dr. Watson: OK. So what’s next?
Sherlock Holmes: The new age my friend – the art of listening to customers. The mention of “Big Data” instantly strikes up an image of bits and bytes zooming around at supersonic speeds to the dreamier folks. To the realists, it comprises of high volumes of data, being tapped at even higher frequencies and from a wide variety of sources. Whichever side of the personality trait you might be, one bitter truth that does exist still with big data is that the more you think you got it under control, the closer you inch towards a data fusion bomb, ready to explode. Only few analytical initiatives have been able to successfully harness its power so far, and Social Media Analytics is among them. By the way, this now is the cue to link back to the thought you so patiently held on to a couple of minutes back. So how about making the best of both worlds collaborate with each other.
Dr. Watson: But how do we do that?
Sherlock Holmes: Convergence! Traditional market research helps you categorize customers into meaningful segments and consequently provide the precious dimensions that one needs to know to understand the DNA of a customer segment. And this is where you leverage social media analytics to scale up massively! Calibrate these dimensions to build models on big data that categorize the voice of current and potential customers, based on what they have been expressing online across various social channels. Tap into what they have been tweeting, posting on Facebook, expressing on various blogs or through product reviews on different e-tailer websites. The mission is convergence, of knowing what each customer persona has been expressing online, everywhere. Scanning for everything that a customer segment writes about, starts yielding unimaginable level of granular insights. You get an ocean of knowledge on offer because you are not restricted by the limited number of questions in a conventional market research anymore. Even the “tracking studies” conducted for model maintenance can become less frequent now since you are already validating on the fly.
To sum up, use a short but crisp Market Research to discover and categorize, and Social Media Analytics to amplify.
Dr. Watson: Excellent!
Sherlock Holmes: Elementary, my dear Watson.