Decision Intelligence

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THE AMAZON WAYA BLUEPRINT FOR SUCCESS.

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Issue 8

• A blueprint for success Part I: 04 The challenge of managing online retail

• A blueprint for success Part II: 06 The Amazon way

• A blueprint for success Part III: 08 How to manage the online channel: An integrated approach

• Adopting a data mindset 18 in a retail organisation

ContentsDecisionIntelligenceThe journal of global commerce

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eCommera is the pioneer of Decision-Intelligent Commerce, enabling retailers everywhere to realise their growth potential.

To find out how we can help grow your eCommerce business contact us at [email protected] or call us on +44 (0)203 530 5800 www.ecommera.com

eCommera

The Trading Intelligence Quarterly is now called Decision Intelligence. To subscribe, visit www.ecommera.com

Welcome to the 8th edition of Decision Intelligence

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We have been publishing the Trading Intelligence Quarterly, now Decision Intelligence, for several years as a forum for discussing the complexities of new retail. The rules for this new world are still being written, but even while the game is still changing, we believe that the winners will be those who adapt their playbook to be built around data.

At eCommera we have coined the term ‘Decision Intelligence’ as being the new imperative for retail. It enables organisations to continually identify and action the most effective decisions, based on all their operational data. So we have re-christened our publication Decision Intelligence. While the name has changed our aim remains the same: to stimulate the understanding and debate about how best to master the new methods and equations of retail. In this first issue of Decision Intelligence we explore management philosophy in today’s retail landscape. We first explain the need for change in ‘ Part I: The challenge of managing online retail’, where

we outline why multi-channel retail is sufficiently distinct from its traditional counterpart that it demands a new management approach. It may be no surprise that Amazon inspired thisissue – all online retailers need to learn how tomanage like Amazon, or ideally better than them.We examine Amazon’s Kaizen-inspired approach to management in ‘ Part II: The Amazon way’. Turning philosophy into practical reality, wemap out our blueprint for retail management inthe article ‘Part III: How to manage the online channel: an integrated approach’. Extrapolating from Amazon’s approach, we dig deeper into what exactly eCommerce managers need to be doing.

We close this issue with an article by data pioneers Andreas Weigend, former Chief Scientist at Amazon, and Gam Dias, Principal of First Retail. They explain why and how retail organisations should underpin their management philosophy with a data mindset.

We hope that you find this issue thought-provoking. We would be delighted to have the opportunity to discuss how we can help you grow your business with the right products, services and insight.

Andrew McGregor –CEO and Co-founder, eCommera

A blueprint for success Part I The challenge of managing online retailMichael Ross – Co-founder and Chief Scientist, eCommera

The Leader

I wanna be the leader I wanna be the leader Can I be the leader? Can I? I can? Promise? Promise? Yippee I’m the leader I’m the leader OK what shall we do?Roger McGough

The retail management challenge may sound simple: how to choreograph the myriad activities required to make a retail business work, grow and make money. Good retail managers know how to plan, how to make decisions, how to take action, what data to collect, what metrics to track and how to review performance.

However, the online channel is forcing retailers to evolve their approach:

• Planning: Where are we going? How are we going to get there? A new growth dynamic driven by customer acquisition and retention, where plans need to line up across marketing, product and customers.

• Acting: What do we need to do? How do we make stuff happen? A new set of activities and associated costs that are often process-heavy, analytically complex and intermingle previously separable activities.

• Measuring: How do we understand what’s happened and what to focus on? A new set of unfamiliar metrics that often give the illusion of insight.

• Reviewing: What’s worked well and what needs to change? A tsunami of data that offers huge potential for insight but often frustrates in practice.

Adapting to this new reality is a real challenge for anyone selling online to consumers – whether traditional retailers or brand owners. Applying a traditional retail playbook in the new customer-centric, interconnected, data-everywhere world is a recipe for failure.

This article outlines the management challenge: why is managing the online channel different (and harder) than offline?

Offline management is relatively easy (or well understood) and many retailers do it very wellThere are lots of successful retailers in the world. Many have been big and profitable over a very long period. Consequently, the management of retail is a well-established discipline. A good illustration is the Nordstrom Employee Handbook which was, for many years, a single 5x8 inch card containing one rule:

Nordstrom RulesRule # 1: Use best judgement in all situations. There will be no

additional rules.

Please feel free to ask your department manager, store manager, or division general

manager any question at any time.

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This works in an environment where:1) Planning is conceptually easy. Growth is driven

by small annual improvements and new stores.2) Actions are well understood. Managers often

have direct experience of the activities they are managing.

3) Measuring outcomes tells the story. It’s easy to review performance over time, across peers, across stores, and across categories.

4) Reviewing the impact of actions is observable (both people and stores). Plus there’s lots of history of what success looks like.

Online retail is exponentially more complex to manage

I was CEO of Figleaves.com from zero until c. £25m turnover. I recall a point in 2003, looking out at our head office staff of 50 people in marketing, merchandising, buying and site operations. All were busily tapping away at their computers. It was impossible to tell if specific individuals were doing a great job, an OK job or a terrible job (and it turned out we had people in all three categories). The challenge was that their activities were so new, and so interconnected that it was hard to work out what was going on – were we overstocked because we’d bought the wrong things, too many of the right things or our marketing team was driving the wrong traffic? We weren’t meeting our target of 50 per cent growth – were we doing something wrong or was the budget unachievable? Our conversion rate was down year on year but traffic had doubled – was that good, bad or irrelevant?

Looking back 10 years later, I can see that this characterises the challenge: the reason online management is so difficult is the number of new and different activities to manage.

• New but not difficult (although easy to do badly). For example, product publishing – ensuring that products are efficiently ingested onto the website

Decision Intelligence. The Amazon way: A blueprint for success

and that product coding (photography, attributes, descriptions) are done promptly and to a high standard. This turns out to be easy to do badly and many simple things can go wrong: products gathering dust in the warehouse because they are missing a data field, products that can never be found on the site because they’ve been miscoded or products that are un-buyable due to lack of images.

• Difficult to optimise (due to complexity). For example, working out when to send a ‘win-back’ email to a customer and how much value to offer him/her is mathematically extremely complex. Many retailers simply treat customers who haven’t purchased for 6 months as ‘lapsing’ and those that haven’t purchased for 12 months as lapsed. They then broadcast ever more desperate (and blunt) promotions to win them back. Clearly, a more nuanced approach is to understand individual customers’ purchasing dynamics and target them accordingly.

• Interconnected actions that challenge the organisation, and where it’s easy to fall between departmental cracks. For example, when a product isn’t selling online, you need to work out if it’s because it’s not on the site (warehouse), it’s not being viewed (product visibility), it’s not appearing in search results (product set-up) or it’s simply not being purchased (price/availability/image/description). These are typically owned by different parts of the business.

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Physical retail is visible and comparable, while online retail is less visible and more difficult to compare. The simplicity of physical retail is being replaced with the multiple interconnected processes of online. It is difficult to overstate the shift in management required to navigate this new and unfamiliar world. But it is not an insurmountable challenge; we can learn quickly from retailers already paving the way.

A blueprint for success Part II The Amazon way

Amazon has recognised that eCommerce management has more in common with manufacturing than traditional retail. It has learnt more about management from Toyota than from Tesco.

Online retailing is like a production process, with interrelated processes, waste, inefficiency and statistical outliers. This challenge is very familiar in manufacturing. In fact, the entire development of Kaizen/Lean (the Toyota production system) was focused on eliminating inefficiency and waste from car production.

Amazon has applied the same principles to every aspect of its operations.

Planning

“ For 2010, we have 452 detailed goals with owners, deliverables, and targeted completion dates…..Taken as a whole, the set of goals is indicative of our fundamental approach. Start with customers, and work backwards. Listen to customers, but don’t just listen to customers – also invent on their behalf.” Jeff Bezos, Letter to Shareholders, 2010

Amazon has recognised the criticality of planning at the input level. There are complex interactions between customer, site, product and marketing decisions. Planning based on outcomes makes it impossible to work out exactly what each team needs to deliver.

Amazon is the world’s leading online retailer grossing $61 billion in sales in 2012. Even more impressively, the Gross Merchandise Value (GMV) passing through the Amazon shopping basket is estimated at c. $90-100 billion – including the Amazon marketplace where only commission is recorded as Amazon revenue.

“ Something we haven’t talked about, but that is super important in our culture, is the focus on defect reduction and execution. It’s one of the reasons that we have been successful for customers. That is something I had to learn about… Well, by “learn” I mean I literally learned a bunch of techniques, like Six Sigma and lean manufacturing and other incredibly useful approaches. I’m very detail oriented by nature, so I have the right instincts to be an acceptable operator, but I didn’t have the tools to create repeatable processes and to know where those processes made sense.1” Jeff Bezos, Founder & CEO, Amazon Interview with Harvard Business Review, 2007

Amazon has been a pioneer in many areas of eCommerce – from technology and operations to site experience and pricing. But at the core of its success has been the reinvention of retail management.

1 Kirby, J and Stewart TA (2007). The Institutional Yes Harvard Business Review

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Acting

“ We look at the number of customer contacts per unit sold. Our customers don’t contact us unless something’s wrong, so we want that number to move down – and it has gone down every year for 12 years. That’s big-time process management. We try to implement those kinds of processes in various places. They’re most naturally applied in our fulfilment centers and in customer service and so on, but we’ve actually found that they can be useful in a bunch of different things.2” Jeff Bezos, Interview with Harvard Business Review, 2007

The Amazon discipline is to hold people to account for their inputs, and align the organisation around them. The Amazon mantra is that you will never get fired for missing a sales number, only for missing your inputs. It understands that holding people to account for things they can’t control is a recipe for failure (or dysfunctional behaviour).

Measuring

“ Senior leaders that are new to Amazon are often surprised by how little time we spend discussing actual financial results or debating projected financial outputs. To be clear, we take these financial outputs seriously, but we believe that focusing our energy on the controllable inputs to our business is the most effective way to maximize financial outputs over time.” Jeff Bezos, Amazon Interview with Harvard Business Review, 2007

Amazon is one of the few (and possibly only) retailers in the world where the CEO doesn’t obsess about conversion rate. In Amazon-land, you can cry about conversion but you can’t directly do anything about it. The Amazon discipline is to measure things that are actionable – if a measure doesn’t directly drive an action, they don’t talk about it.

Reviewing

“ At a fulfilment centre recently, one of our Kaizen experts asked me, “I’m in favour of a clean fulfilment centre, but why are you cleaning? Why don’t you eliminate the source of dirt?” Jeff Bezos, Letter to Shareholders, 2008

Many retailers spend their time reviewing what’s happened. By contrast, the Amazon culture is to focus on what you can actually do about it. Input metrics are continually evolved and refined. The key to Amazon’s success is the relentless focus on the search for the right metrics. “There’s an incredible amount of challenging the other person,” says Manfred Bluemel, a former senior market researcher at Amazon. “You want to have absolute certainty about what you are saying. If you can stand a barrage of questions, then you have picked the right metric. But you had better have your stuff together. The best number wins.”

***

Amazon has provided many of the answers for online retail managers. It is not a blank canvas, we have an inspiration to aim for and learn from… and who knows, eventually outperform!

2 Kirby & Stewart, 2007


A blueprint for success Part III How to manage the online channel: An integrated approach

The challenge for managers is to develop a coherent approach that coordinates the multiple interconnections of the online channel. Traditional methods won’t work. We believe that a totally new management framework is required. If you get it right growth is more predictable, actions are co-ordinated and executed, diagnosing performance is straightforward (you know why business is good or bad), and the review process is comprehensive (there’s nowhere to hide).

At the heart of the approach are four iterative steps that need to be applied to the five core trading

elements of eCommerce (customer, marketing, site, product, operations):

1) Plan: Where are we going? How are we going to get there?

2) Act: What do we need to do? How do we make stuff happen?

3) Measure: How do we instrument the business to understand what’s happened and where to focus?

4) Review: What’s worked well and what needs to change?

“ Give me grace to accept with serenity the things that cannot be changed, courage to change the things which should be changed, and the wisdom to know the one from the other.” – Reinhold Niebuhr

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Planning in online retail is difficult

Common pitfall #1: Plans can be unrealisticPlans based on linear extrapolations of traffic conversion and average order value can lead to disappointment – this is more an exercise in multiplication than planning. And plans based on high level market growth percentages are no better. The board needs to understand the engine of customer-centric growth, and the dynamics of customer acquisition, retention and churn.

Common pitfall #2: Failing to understand the trade-off between growth versus profitRetailers like the thought of accelerating growth and reducing marketing spend. Unfortunately, this doesn’t work online. Acquiring and retaining customers costs money. So it’s critical to understand the marketing payback (do you break even on the first order, after a month or a year), and to set the right trajectory that maximises cash, short-term profit or long term value. There is no right answer as this trade-off will always be a question for the board and shareholders.

Common pitfall #3: Plans are not joined-up It is remarkably easy for plans to be unachievable if there is no interconnection between product, marketing and customers. Product plans (breadth, depth, new brands, new categories) are critical to widen the customer acquisition net, and increase sales to existing customers. Marketing plans need to align with customer acquisition and retention targets.

Common pitfall #4: Viewing technology as an annoying cost Smart retailers – with Amazon as the smartest – understand the criticality of technology as an enabler which allows the retailer to get more done faster by fewer people.

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This approach is inspired by Amazon which has, in turn, been inspired by the process revolution that emerged from Toyota (Kaizen/Lean) and Motorola (Six Sigma). At its heart is a focus on granular instrumentation, process optimisation and continuous improvement. This is the only way to ensure that the business is joined up.

Here we examine each of the four steps in turn: why it is important, what typical challenges will arise, and how to approach it for a successful outcome.

Plan

“ Everybody has a plan until they get punched in the face.” – Mike Tyson

“ For 2010, we have 452 detailed goals with owners, deliverables, and targeted completion dates…..Taken as a whole, the set of goals is indicative of our fundamental approach. Start with customers, and work backwards. Listen to customers, but don’t just listen to customers – also invent on their behalf.” – Jeff Bezos, letter to shareholders, 2010

It’s hard to argue about the importance of planning. It is vital for everyone in a business to be clear on where you are trying to get to (destination) and how you are going to get there (deliverables).


There are hundreds of activities that need to be done to make an eCommerce business work, and thousands of small actions that might be worth doing.

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• How much revenue do we expect from existing customers next year? • How many new customers do we need to hit our target?• How much should we invest in customer acquisition versus retention?• What is our share of addressable spend of our high value customers?

• What range and stock do we need to deliver our target? • Does the product launch schedule align with the revenue profile? • Do we have the right brands/categories to acquire new customers?• Do we have the right brands/categories to retain our high value customers?

• What marketing budget is required to deliver the customer acquisition and retention targets?

• How should the acquisition and retention budgets be allocated across marketing channels?

• Is the marketing calendar joined up with product and customer?

• What new functionality will be added to the site?• What benefit will be expected by when?• Is the site refresh cycle aligned with customer behaviour?

• Is the resource plan aligned to deliver an exceptional customer experience? • How do we make the right trade-off of service versus cost?• Does the daily/hourly operational plan align with planned major

marketing activity?


Separate plans are needed for each of the five core trading elements of the business: The key to a good plan is to ensure coherence across all areas of the business. The table below gives an overview of the questions that a plan should address.

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Much can go wrong with taking action

Common pitfall #1: Making decisions

• Bad decisions It’s easy for bad decisions to be buried or obfuscated: when to send a promotion, how often to change a landing page, or how much to spend on a keyword. Often ‘best practice’ is invoked – but this is meaningless in an industry only 15 years old where few even know what good looks like.

• Short-termist Retailers like to hit their numbers and this can create an urgency to drive sales today. It is easy to drive sales online by giving money to customers. Whilst – on the high street – it’s easy to tell the difference between a premium and a value retailer (always on sale) the same is not true online. Promotions and offers can be pushed through targeted marketing channels whilst keeping the storefront ‘clean’. This approach creates three challenges: it trains customers to expect discounts; it flatters sales and creates an unrealistic comp for next year; and it leads to a less loyal customer.

Common pitfall # 2: Doing the right stuff in the right order

• Incomplete Simply being aware of the complete set of things that could be done is a huge challenge. This is closely followed by ensuring that everything that could be done, is being done. In most retailers, there are hundreds of money-making or cost-saving activities that simply aren’t getting done.

3 Kirby & Stewart, 2007

Act

“ The major difference between a thing that might go wrong and a thing that cannot possibly go wrong is that when a thing that cannot possibly go wrong goes wrong it usually turns out to be impossible to get at or repair.” – Douglas Adams

“ …focus on defect reduction and execution… a bunch of techniques, like Six Sigma and lean manufacturing ….tools to create repeatable processes and to know where those processes made sense. Customer contacts per unit sold… has gone down every year for 12 years. That’s big-time process management.”3 – Jeff Bezos

There are hundreds of activities online that need to be done to make an eCommerce business work, and thousands of small actions that might be worth doing. Many of these activities are new and unfamiliar to traditional retailers. Moreover, the evolution of eCommerce technology continues to catalyse ever more new activities. In addition, as businesses scale and technology improves, uneconomic activities become economic. Unfortunately, many activities are often simply not done, done badly, obfuscated by digital natives or fall between organisational cracks.


• Poorly prioritised It’s easy to focus on the shiny new thing before the basics are in place – what one eCommerce leader describes as “icing on the cake for which we do not yet have the ingredients”. Experience is required to know what the basics are and management discipline to get the basics right first.

Common pitfall #3: Executing effectively

• Under resourced The resource required to manage an online store is variable by many factors, including: number of products, customers, keywords or landing pages. Often CEOs have created the smallest online team possible to ‘keep the lights on’ – this is a false economy, you need sufficient resource to evolve.

• Unclear ownership Things can go wrong very quickly if actions are either not owned, or have multiple owners with subtlely conflicting objectives. For example, who should own product sort orders on the web site – the merchandiser who wants to drive gross margin, or the site manager focused on conversion rate?

• Poorly executed Ensuring high quality execution is particularly challenging where many of the actions cannot be observed. For example, an electronics retailer identified that a thousand key products were receiving no traffic from Google. The resolution was to change match type rules buried in a bid management system. This was a critical task but it was extraordinarily difficult to give the CEO visibility that it had been executed, and that the problem would not repeat.

• Blame technology Execution should be constrained but not excused by technology. It’s easy for people to make excuses of what can’t be done, rather than what can. And it’s critical that technology is seen as an enabler.

A coherent approach to taking action

The nature of online – characterised by huge variations in customer behaviour – is to create a long tail across all areas of the business: such as keywords searched on Google, unique searches on the site, groups of products viewed or pathways through the site. The flip-side is an ability to take very granular actions to optimise; increasingly, technology enables both micro-optimisation and personalisation. Averages and aggregations are the enemy – they obfuscate issues and opportunities.

It’s always good to action the top 50 (keywords, products, searches, pathways) but it’s also critical to understand the opportunities to take action on the long tail. That is why it is so important to understand statistical outliers. You need to compare the online store against itself, rather than having lots of stores to compare, and the greater the variation, the greater the opportunity to optimise.

Our approach is to first define the totality of things that can be done. Each action then has to be understood and assessed by various criteria: easy versus hard; tactical versus strategic; no-brainers versus payback investments; and ‘do versus don’t touch’ technology. Critically, the frequency of action changes with scale – a monthly activity for a small retailer might be actioned daily by a larger retailer. Managers then need to be extremely disciplined at understanding the constraints on delivery – people/process/technology – and to then create a culture of delivery.


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• Retailers need to think about customers as individuals and think in terms of ‘customer next best action’– ‘given everything we know about this customer, what do we do next?’

• Customers may be aggregated into segments to take action, but this should be an outcome, not the starting point

• The manner in which products are presented and found online requires retailers to think about actions at product-level, product-colour level and SKU-level

• This includes: managing product sort orders, deciding when to delist colours, merchandising broken ranges, deciding when to move stock between stores and online, managing inbound traffic to specific products etc

• Retailers need to optimise at the most granular level possible for each marketing channel:

– It’s dangerous to optimise ‘Google’ at an aggregate level when the real action happens on a keyword by keyword basis. Moreover, it’s also critical to work out what to optimise at keyword-level, ad group-level and campaign-level.

– Affiliates need to be looked at both by type (cashback, voucher, content, shopping engine) and individually

– Retargeting advertising has to be optimised based on the creative, publisher and customers/visitors being retargeted

• More generally, every channel has its own tactics to maximise profitability

• Optimising the site requires on-going detailed funnel review – by step, by customer, by geography, by entry point – to understand where the bottlenecks are. Data needs to be combined with anecdote from site surveys, usability tests or site recording tools

• Site actions need to taken: by individual page, by page-type and by customer

• Every stage of the order process from point of order to receipt of goods by customer needs to monitored and continuously improved

• Critical actions are the monitoring of all service failures, and causes of in-bound service enquiries

The table below highlights the granularity of action for each area:


ambitious enough!). Metrics like conversion rate are bandied around but the reality is that they are devoid of information. Why?

a) Conversion gives an incomplete picture. Conversion rate is but one component of the online profit tree. At the very least, conversion rates always need to be considered in the context of visits and profit per order. Conversion going down may not be a bad thing.

b) Conversion is an outcome of having the right

products, pricing, availability, site experience and service. You cannot improve conversion ‘in one go’, but you can improve the individual and interconnected components: new/exclusive products, better availability, improved checkout flow, quicker delivery, and better service for VIP customers.

c) Conversion is naturally volatile. It’s a function of the source, destination and quality of traffic so the conversion rate online is always volatile, driven by the mix of traffic. Conversion must be systematically de-averaged to distinguish between benign fluctuations of mix, and more interesting absolute trends.

Common pitfall #2: Using the wrong inputs

To make sense of the online world, you need to create a hierarchy of metrics – outcomes that tell you what’s happened at a high level; inputs that tell you what’s been done, and where to focus. However, it’s not quite that easy – there are three types of inputs to avoid:

• Meaningless metrics whose behaviour is not necessarily good or bad. A good example: percentage of orders in a week from repeat customers. If this increases, it could be either a good thing or a bad thing; it depends. It’s easy to increase it by acquiring fewer new customers.

Measure

“ What gets measured, gets managed.” – Peter Drucker

“ Senior leaders that are new to Amazon are often surprised by how little time we spend discussing actual financial results or debating projected financial outputs. To be clear, we take these financial outputs seriously, but we believe that focusing our energy on the controllable inputs to our business is the most effective way to maximize financial outputs over time.” – Jeff Bezos, letter to shareholders, 2010

Knowing what to measure is crucial in the complex, interconnected world of online retail. Measurement is critical to understanding what’s happened and to work out where to focus – it requires turning the vast tsunami of online data into something meaningful.

Measurement in online retail is challenging – data is interconnected, complex and comes in vast quantities. Data is often incomplete and lives in silos. Perfect is often the enemy of good enough. Data (the raw material) needs to be turned into information (something useful) and then presented in a dashboard that makes it easy to interpret.

Common pitfall #1: Measuring outcomes

In physical retail, simple outcomes tell the story – a clear picture emerges of performance across stores, across categories, across buyers and across managers. Metrics are mature and benchmarks are plentiful and meaningful.

Online is a different story – outcomes online give only the illusion of information. Outcomes are great when all the numbers are being hit, but they tell you nothing when numbers are missed (and if no numbers are ever missed, you are not being


• Overly aggregated metrics where you end up with an average of an average. A good example of this is quoting a Google cost per order – the challenge here is that (i) averaging trademark and non-trademark keywords obfuscates performance, (ii) it gives no sense of margin/profit/customer value, (iii) it gives no sense of volume.

• Self-congratulatory metrics which are akin to marking your own homework. An eCommerce operation whose primary metric is percentage of orders dispatched on-time and in-full (OTIF) may often give a false sense of success. The challenge is definitional – exactly how “on-time” is defined. Typically it is the company’s definition of on-time, not the customer’s.

The solution is to measure input metrics across the five trading elements

The online challenge is to create a metric hierarchy that connects target outcomes (revenue/profit) with controllable inputs (things that people can affect). It is the only way to make sense of what’s happened, and help inform where to focus. The metrics below are some examples – success can only be declared when you can assert the following:

• There’s nowhere to hide. Everything good/bad/ugly that happens will show up in one or more of the metrics

• There’s a clear distinction between inputs and outputs, and that all inputs are clearly owned by an individual and are controllable

• All inputs should be ‘monotonic’ – up/down is good/bad – if an explanation is required or up/down could be good or bad, it’s the wrong metric.

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• Long run 1st to 2nd purchase rate• Average time from 1st to 2nd purchase• Number of customers past expected lapse point

• Percentage of orders with promotions• Availability weighted by page view• Percentage stock not viewed• Percentage stock viewed but not purchased

• New customer acquisition cost• Cash payback for new customers• Marketing spend which generates no orders• Bounce rate from paid marketing sources

• Number of searches with no first page click• Number of searches with no filters applied• Number of pages with no inbound SEO traffic• Number of broken links

• Percentage orders shipped on promise• Average order cycle time• First contact resolution• Contacts per order

Example input metrics:


Review

“ In God we trust, all others bring data.” – Edward Deming

“ At a fulfilment centre recently, one of our Kaizen experts asked me, ‘I’m in favour of a clean fulfilment centre, but why are you cleaning? Why don’t you eliminate the source of dirt?’” – Jeff Bezos, letter to shareholders, 2010

Review is necessary to ensure continuous improvement: how to gain real insight from the data, diagnose the underlying causes of performance (good or bad), and discover new insights. Today’s insight should be tomorrow’s business as usual.

A great example comes from Sky Television when it observed both high churn and high inbound customer service enquiries. The typical response would have been to offer retention discounts and increase the call centre headcount. But Sky worked out that the root cause was poor set-top box quality. The action was obvious: invest more into box manufacturing. This sort of insight is clearly transformational – churn and customer service queries declined and the rest is history: Sky is one of the most successful customer-centric businesses in the world.

Reviewing an online business can be difficult. Physical retail is characterised by anonymous transactions, good enough data and insight based on gut and experience. Whilst retailers such as Tesco have used data to build great businesses, many retailers have succeeded without a data-driven approach.

Conversely, online there are bigger opportunities and threats: the opportunity to have a complete insight into customers, products and marketing, and the threat that your competitors may ‘out-insight’ you. For example, they could work out that certain types of customers are high value and then outspend you on performance marketing. Or they could develop some deep customer insight and then communicate to your customers in a more relevant and timely manner (raising customers’ expectations in the process).

There are many challenges to gaining insight from online data. The nature of eCommerce data makes it extremely powerful and equally dangerous – it’s very easy to misinterpret data or simply draw the wrong conclusions. This is a battle for insight.

Common pitfall #1: Reverse causality – confusing correlation and causality

• Customer service contact does not increase customer value A retailer observed that customers who contacted the call centre had a high correlation with lifetime value. Its hypothesis led to encouraging customers to have a customer service interaction as a way to increase their value. Unfortunately, it turned out the causality was reversed: their loyal/engaged customers were simply the ones most likely to complain about an issue – i.e., it was their value that drove their contact, not their contact that drove the value.

• Keyword match type does not drive profitability A retailer reviewed profitability by match type (exact versus phrase versus broad) and found that keywords on phrase match had significantly higher profitability than keywords on exact


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match. Their plan was to move more spend to phrase match which unfortunately turned out to be the wrong answer. In fact it was search intent that was driving profitability: long tail keywords were being picked up on phrase match (and turned out to be very profitable) so it was the specificity of the search phrase that was driving profitability, not the match type.

Common pitfall #2: Hidden factors/confounders – the interconnectedness of data can mean the answer is not where you expect

• Availability not site Figleaves observed a declining site conversion rate and spent weeks trying to identify issues with site usability. The issue turned out to be product availability, which was only exposed when we started measuring page-weighted availability – we were regularly going out of stock of our highest viewed products.

• Operations not marketing Barnes and Noble observed a low customer retention rate which they hypothesised was to do with a failing CRM plan. It turned out to be nothing to do with marketing, and was driven by a failure to deliver on promise. Customers were promised books in 2-3 days and they were arriving after 4-5 days.

Common pitfall #3: Bad analysis – look at the data in the right way, or risk a ‘sign-flip’ where the wrong analysis will lead you to take the entirely wrong action:

• Product versus customers Figleaves worked out that many of our brands – such as La Perla – looked unprofitable but were great sources of new high value customers. In 2008, the new management at Figleaves applied the tried and tested principles of Tesco’s

merchandising management to premium multi-brand lingerie. They ranked the suppliers by profitability and delisted the unprofitable ones – La Perla was cut, along with many others. The business lost its most valuable customers.

• Product versus customers

A department store observed that customers buying TVs as their first purchase often only bought TVs. But customers who bought beds often also bought pillows, duvets, sheets and TVs (the hypothesis was that a new bed was a trigger that they were redoing a room). The insight was to price beds as a customer acquisition mechanism.

The solution is to institutionalise insight

It is often the case that insight is rationed – there is a limited pool of analysts and any request for analysis has to join the queue. And it’s often only available to the people who shout the loudest. In addition, a typical analyst team is good at answering specific questions but is not set up for the more iterative and creative process of data and insight discovery.

Moreover, having insights alone is not enough – the key to success is to institutionalise them (and make them part of the way the business is run). Lands’ End observed that their customer acquisition team was meeting its target by simply ‘buying’ transactions. The insight was to redefine a new customer as one who’s made two purchases and incentivise the customer acquisition team accordingly. “They’re not a customer until they’ve made two purchases” is part of the DNA of Lands’ End.

On the following page are some examples of the questions that are solvable in the online channel. The key is to solve them, institutionalise them and then to keep solving.

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Customers

Products

Marketing

Site

Operations

• What are the brands purchased by your most valuable customers?• What marketing channels deliver your most valuable customers?• Where should we invest the next £ in order to drive better customer

retention?

• What products/brands are bought repeatedly by the same people (gems) versus which ones are only bought once (dogs)?

• Which brands are incremental versus cannibalise?• What’s the optimal range size?

• Which marketing pathways acquire the highest value customers?• What is the optimal marketing spend?• What is the optimal allocation of marketing spend?

• Which elements of the site are being refreshed too frequently?• Where are the blockages?• Which technology enhancements will drive true incremental sales?

• What are the causes of inbound customer service?• What’s the optimal trade-off of cost versus service quality?• What can be automated or prevented?

* * *

Many industries have gone through an ‘analytics epiphany’ where the management discipline had to evolve – from banking, insurance and airlines to hotels, consumer goods and supermarkets.

Today, it’s the online channel that is being transformed. Five years ago, simply generating revenue online was considered success. No longer. Today, the online channel is a critical part of the retail landscape and needs to be a source of growth and profit.

In many ways this is merely the warm-up. As online becomes ever more ubiquitous within physical retail, the management disciplines described here will need to be applied to the whole of retail.

Retailers will have to adapt to a new way of operating their business or risk a very precarious future.

Examples of the questions that are solvable in the online channel:


Adopting a data mindset in a retail organisation Andreas Weigend – Director of the Social Data Lab, Stanford University

Gam Dias – Principal, First Retail

The constant headlines about ‘the retail landscape undergoing profound changes’ tend to be accompanied by the prescription of bigger and more complicated information systems to handle the ever-increasing quantities of data. Yes, brute force data processing does provide a return on investment. However, the greatest returns are often from less conventional business approaches to data, driven by an organisational culture that thinks about how to get and use data in every aspect of its operations.

This article explains how to adopt a data mindset – one of the most critical management challenges facing online retailers today.

1. What is a ‘data mindset’?2. The data champion3. Get more data, give more data4. Data for continuous improvement

What is a data mindset?

When an organisation has a data mindset, every single person working there, from the CEO to the cleaner, uses data to inform their decisions. Agreement is required for when data should not be shared, rather than when it should. Access is easy and fast, with no need to go through IT departments and write SQL queries.

It is a fundamental shift, and there is often a real fear about the potential loss of control. Doc Searls, co-author of The Cluetrain Manifesto and author of Intention Economy, likens it to the 1980s when mainframe-centric IT departments fought against PCs being introduced and the 1990s when HR departments opposed employees gaining Internet access.

Amazon

When website content was carefully authored by each retailer, Amazon pioneered customer product reviews creating a resource for online shoppers that became more influential than authoured descriptions.

BestBuy

As online retailers focused on SEO searchability projects, BestBuy’s OPEN team offered an API to product data and local store inventory data which allowed developers to remix the BestBuy store into stores of their own – a success story in increasing product ‘findability’.

Walmart

At a time where retailers used sales knowledge to negotiate better terms with suppliers, Walmart shared sales data with suppliers to increase availability.

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Historically, retail managers’ most significant business decisions were capital intensive with long cycle times – enter a new geography or market; build a new distribution centre; or open 20 new stores. The final decision was based on careful research, usually by some expensive analysts. Today, two things have changed in the decision making process:

A) Shorter cycle times We simply add capacity in the cloud or launch via an online marketplace. Today’s business is driven by many smaller and specific steps, each of which is measurable.

B) Cheaper cost of analysis There are more data, more tools and more skills available to carry out analysis. Entering a new market no longer requires a market segmentation by an analyst firm and locally based advertising; today Facebook Graph advertising does it for free, in hours rather than weeks.

These same shifts in data use can be seen in Formula 1. Telematics now send back data as the car is driving, not after the race, which allows the engine to be adjusted continually throughout the race. Retail is rapidly transforming its pace of decision making in the same way.

The data champion

How data is thought about, gathered and used is a strategic decision for every organisation, and should be driven by a data champion from the top – but where at the top? The CIO, as the data protector, works to keep people away from the data. The CTO, responsible for the integrity of systems restricts system access. While the CFO is concerned with reporting using as little information as possible!

Many retail organisations, perhaps inspired by Amazon, have created a Chief Scientist role. This role reverses the scientific method by focusing on asking questions rather than finding answers. Answers, like data, are commodities. Being able to ask the right question is the creative element that will allow you to set your business apart. While this role is a major step forward in developing a data mindset, the Chief Scientist cannot be the data champion.

The scale of cultural change requisite to become a truly data-focused organisation must come from the CEO. It’s a massive shift to make every employee customer-centric, and encourage them all to actively gather and use data to drive the business.

Get more data

Many retailers are overwhelmed by the amount of data they have today; we argue that it’s not enough! Having a data mindset demands the continuous search for more data and more ways of using that data.

How can you get more data?

• Start with your customers. Make it easy for them to tell you more. At Amazon Bezos believed in removing all barriers to contribution and so we allowed customers to write reviews without a sign-in.


• Use keywords and phrases from site searches – they can help stock control and product indexing, and over time help to decide what new products to add or where money can be made running PPC advertising for other retailers.

• Review internally what technology is needed to help every part of the business contribute to the data pool. Can you install in-store cameras to examine queuing and checkout and re-deploy resources real time to minimise customer wait time? How can shrinkage be measured in the supply chain or store? Can we use predictive analytics to determine where theft is likely to occur next?

• Identify external sources of data that will provide new competitive insights. The Social Graph is a great source of data about customers and their

social networks and the online advertising game is now allowing retailers to target ‘look-alike’ audiences. What would happen if adjacent retailers were able to share information in a co-optition model?

Looking outside retail, there are also plenty of businesses using data exhaust (the data produced as a by-product of another activity) to great effect. Google indexes the web and allows people to search it for free. This data exhaust is an aggregation of search words which are then used in an Adwords auction search term to advertisers.

LinkedIn allows people to upload, store, update and share their CVs. This data exhaust is an aggregation of the movement of people between companies, which recruiters pay to advertise and find potential candidates.

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New Data Sources

Sensors: New sensor data provides real time web-style analytics from movements and sound levels inside and outside stores. Correlated with point of sale systems, real time actions can be taken to optimise store performance.

In-page tracking: Real-time monitors of keyboard, mouse and click data on websites can be used to provide aggregated and granular observation as to successful and unsuccessful visits to the website and can be used to provide a more dynamic site experience.

HyperLocal: Where we are in relation to local landmarks and features, in relation to each other and in relation to the retail presence will create different modes of consumer behaviour that can be used to build a better customer experience.

Intention: Consumers are now starting to explicitly state their wants, needs and intentions and inventories of demand are now appearing implicitly.


Give more

It may feel counter-intuitive, but you should share your data with your suppliers and partners, as well as your customers. It will empower decision-making all along the chain.

• To suppliers: Sharing data with your supplier network will help them action improvements and optimise processes to provide a better service. For example, Walmart shares its sales data with its suppliers to help them better predict demand and be proactive in ensuring availability.

• To customers: Guide your customers buying decisions. Sears Holdings has a large base of customer data that they offer to other retailers implicitly via ShopYourWay and explicitly via Metascale. Rather than intrusive push campaigns, customers are presented with products that are relevant and perhaps outside the Sears’ assortment while they are browsing. Sears also benefits from getting feedback into their online marketplace on which new products to offer.

Use your data to improve your processes

Design your processes to capture more data so that you can further improve your processes. Amazon actively harvests consumer intelligence. For example they regularly examine on-site search terms as part of the process to improve product descriptions.

If you put the right system in place, like the Social Data Intelligence Test, your products can improve directly from customer data. Customer service should be a profit centre, not a cost centre. If customer feedback data is provided quickly and easily to buyers, suppliers and designers they can respond rapidly.

Online retailers use natural feedback loops such as customer reviews and crowd-sourced support forums that allow customers to engage with them and simultaneously improve the product or experience. For instance, Sony Entertainment uses the gaming feedback boards (e.g. IGN) to determine what features customers love and hate and to work out the optimal time to launch. Google maps experienced a problem with users hacking into their system and turned this into an opportunity by opening up the system, allowing people to contribute – which has allowed for a better product.

***

Introducing a data mind set is a cultural shift for many retail businesses. The CEO has to introduce a programme of behavioural change where every decision and every meeting is led by data. It should be expected and indeed demanded. Early activities to get you going may include: openly acknowledging data-led successes – where has money been made or saved?; cataloguing initiatives which are explicitly data-led (either new analysis of existing data or collecting new data); or explicitly gathering and sharing widely the data generated from every new product or service launch.

The Social Data Intelligence Test

The Social Data Intelligence Test is a diagnostic tool to assess how an organisation tracks, analyses, and acts on ‘social data’ – information that consumers knowingly and willingly share. This test was developed by The Social Data Lab at Stanford University which helps companies understand the irreversible impact of the Social Data Revolution on individuals, business, and society.


“ One thing DynamicAction has taught us is that there’s a huge number of opportunities even within what we thought was a reasonably well run operation – there’s quite an awe inspiring number of issues which are uncovered by DynamicAction… and they all relate to the business we do now rather than what we might be doing in the future which is very inward looking. So rather than investing in 4 new sites, we think there’s a lot more to be made out of our current operation.”

Andrew Mossman, Director of Home Shopping, TM Lewin

Visit www.ecommera.com/products/dynamicaction for more information

DynamicActionPart of your formulafor success.

Contact Bethan Fryer at [email protected]

Tel: (0)203 530 [email protected]

DynamicAction, a decision intelligence product, helps eCommerce teams work together to accelerate online sales and profitability by:

l Merging the data from the many silos across the eCommerce operation

l Presenting a single integrated view of eCommerce business

l Identifying the root causes of fluctuations in online sales

l Creating team worklists prioritised by their impact on profit

With DynamicAction your eCommerce team will be focused and co-ordinated around the insight that accelerates growth.

DynamicAction

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