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Alpha, Beta, Consumer and Data Cultures

Updated: Mar 12, 2020

My retail experience in Alpha, Beta, Consumer, Data Cultures


What makes a shop work is an art and a science. It’s knowing your customer and servicing them profitably but also directing them and giving them much more than a product. But there is something interesting happening on the retail landscape. Whilst the big players are getting overly scientific and the indies have little time to apply the science, consumers are waking up, so here are a few of my behind the scenes experiences that I hope might influence your shopping behaviour this Christmas!


Alpha cultures

For me the high street is a precious space. It's where people meet and enjoy social interaction, they see the quality in design and materials and services and they invest their hard earned readies in those products/services.


For some people the shed is a special place. When the internet made the shed (synonyms for warehouse) an important retail landscape for servicing shopper's convenience, many things shifted in the costing of retail. Unfortunately, people (me included) were too busy with their smartphones and the IoT to lobby for real governance. Big retailers suckered up the costs and invested in multichannel, omnichannel, and now Artificial Intelligence (AI). For a while the big players were able to offer free delivery on items that cost more to transit than they cost the customer, because, on the whole, servicing customers directly from the shed to their home was more cost effective than, the risk of putting stock in multiple stores; paying for customer services; and covering the cost of shrinkage.


When you run everything like a cost centre you start to lose what makes investing in stock an art form. What’s more of an illusion to consumers is that some products sold on some of our best loved shopping websites never even touched the sides of the retailers selling them. Some goods are dispatched, directly from the suppliers shed. And what if the supplier failed to deliver the goods within the strict deadlines imposed on them by the retailer? Yes you’ve guessed it - the big players would try and fine the distributor! Online created new revenue streams to cover the cost of ever squeezed margins that are incurred in running a bricks and mortar shop.


When you treat a communal social space, like a shed, you’ve lost a lot of art form and a special space. There is no room for creative intuition. It’s just calculations and applying a formula.


Beta cultures

Beta culture is a defined thing - it’s not something I've made up for this article. It’s a tech term to recognise that you are running with a piece of technology before it is the absolute finished product.


There was a time in my recent history when people were the masters of activity. The system did not prevent the people from achieving the achievable. The system facilitated people doing a good job. Human intelligence was the driving force behind the operational processes. Whilst we have been busy automating parts of the process, in some instances people have lost out. When I look at the bizaar data flow of information in some companies and ask why, I baulk at the frequency of hearing the dreaded words “because we have always done it like that”. Data and system influencing initiative in sales and operations is at an all time low.


For me, this is part born out of over computerisation and the desire to have fully thinking systems so people don't have to use any thinking power. More beta culture please! My theory of why Millennials have it so tough is that so many things are out of the reach of achievable because businesses are working with a systems that people don't have the desire & confidence to change. I have witnessed so few people able to cheat the system and influence the process, whilst increasingly the senior team are happy to write off huge swathes of money just for the sake of system’s process not currently allowing fluidity of activities. I remember a time when we would build a routine to cheat the system if it saved £20K, now that sum would be written off and in later months, you read profit warning from those kinds of companies.


The investors in people badge might as well have been replaced with the investors in tech badge. Whilst HR teams have invested in training courses like emotional intelligence, how to style your email communication, what personality type are you, they have only just started to wake up to data training.


In big business it is now hard to be nimble and act fast, processes are not fluid. Millennials get a tough press, the rotten systems in work now that need changing, can't be changed without a road map, a framework and mega investment.


Have you heard of an IT project that was delivered on time and in budget? The pace of things is intolerably slow. It need not be. But you have to empower your junior staff to have the skills to be data savvy and support the business in every element of the day to day whilst it changes and many companies are so behind the curve they feeling like busy fools..


Consumer culture

I’ve heard an increasing chatter amongst the AI data scientists that the big players are referring to people as consumers not as customers. AI developments are pretty cool! Personalising motor-home ads to drop into your social media feed within minutes of you mentioning to a friend that it is forecast to rain all weekend and you’re not looking forward to a planned camping trip is pretty convenient right? Even if it is eavesdropping on more private parts of your life? What if your life is missing something though? And that something is it the variety of the new and unknown and of course maybe a little more traditional social interaction?


I don’t think the internet is our preferred method of consumption, it’s just a convenient habit born out of survival. Our long inflexible working contracts, the demising shopping arena, and our consumer lifestyles have all allowed the internet to grow and now there is a race to the bottom on prices where the internet companies have squeezed the margin out of many people competing with them.


For anyone suffering from anxiety and social phobia, then internet shopping really cuts out the need to interact with people. If the press about the mental state of our nation is right, then there is growing customer profile here for the internet players to prey on.


Amazon chiefs will tell you that Amazon’s intelligent Economic Ordering Model algorithm gave the company a head start in e-commerce and even then it took them a while. Many customers were originally naive to Amazon the shop and they were using Amazon as a search engine. All the while this was limiting the users search of the wider world web and feeding Amazons Economic Ordering Model with demand predictions. But just like traditional retailers, even Amazon is not good at predicting what will be new next season and how this will impact existing lines.


The changing consumer cultures are crippling our shops. Wardrobing (the purchasing and wearing of goods and then returning) is no longer the practice of brazen characters who’ll poke the tag inside the garment whilst they wear and then return their outfits. Many people are at it and not because the internet made it easier by removing the barrier to looking in the eye of someone and lying. Younger people who’s options of rising up the ladder and becoming well off are so intangible coupled with their dislike of the growing polarisation in society they actually don’t care for eating into the margins of the perceived fat cats at the top of the chain.


For the retailer, staying well stocked but not overstocked is important to the customer experience. Stock investment is a key metric for retail business. Put this conundrum to a machine learning computer, the algorithm might read:


Dear Computer,

My learning machine friend!

What is the optimum time when one should take a sales reaction in 2019?

Could it be 28 days after the original purchase to allow for wardrobing?

21 days for failed on-time delivery?

14 days to allow for half the bought items to get returned?


It is an increasingly difficult decision for a person or a machine, and the machine will never get the truth!!! AI companies I’m following say that machine learned forecasting is going to allow shops to hold less stock but my prediction is that when it comes to our impatient consumer habits, only online customers who don’t care that their product was shipped through the night from a shed will benefit. Much like the Argos store model of today where the hub and spoke system means that the buying team consider a line to be fully available if it's available within 4 hours through the hub and spoke system. In practice though, for many customers, if the stock is not available there and then when they want it demand plummets as the customer walks out of the door and buys an alternative.


I feel the pain of the John Lewis colleague who told me their shift now consists of booking returned stock from on-line orders back into stock - that is not the customer service job of yester-year. I can empathise after my experience working in the back office of a direct distribution supply. Argos’s Supplier Direct Fulfilment model was torture - the supplier is paid when the customer signs for the delivery of the goods and not the point of dispatch which meant that we were dispatching hundreds of parcels a day, of which, many were delivered within the Service Level Agreement, others were delivered on second or third delivery attempts up to two weeks later; but then eating into the margins, many items were returned to the warehouse after going round the hub system several times and arriving back a dented damaged shell of their original selves. Some just got lost in the system. You could claim back for the ones that just got lost, but you had to do it within a certain time window and if you had experienced the claims procedure you would be inclined to think the process was designed to be so hideous that you inevitably miss the deadline.


Data Cultures

I believe in the newly talked about data culture the same way that I imagine Florence Nightingale did. There is something telling in the numbers across a period of time that is lost in the moment. Especially in a scaled operation. I love the eagerness of medical sector to adopt data but I do think they need to remember their moment is not primarily about measuring data but like in Florence’s example it is about bedside manners and caring for the sick. Chat bots cutting out the role of triage is great cost saving but back to culture, it only helps convenience and the erosion of value of people and cannot therefore positively impact culture.


In the digital age, the touch points of our activity are great and the resulting data is big. It is meaningful? Is it structured and indicative? Should we learn from it even if we can get a computer to make something of it. It’s great that machines can learn from it but in a world where there is an app for everything, someone has to champion people and human capital.


At ivity we champion people’s data literacy and influencing skills. We help people navigate the data and influence their systems to create a better world.


And let’s remember culture eats strategy for breakfast so all activity is futile if you have not put people into the mix, listened to them and developed them.

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