Data triangulation - Driving Profit form Data in Ecommerce

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Presentation from Mike Baxter on data triangulation in ecommerce - over 10 years of consultancy experience with real-world ecommerce data.

'Data overload' is the term increasingly used to describe the biggest challenge and frustration for online retailers. Tackling this data overload issue head-on is what this breakfast seminar is all about - how to leverage just the data you need to maximise profit.

How to focus on the metrics that matter.
Data-driven strategies for maximising profit.
Matching the right products with the right customers.
Accelerating ecommerce performance.

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  • Holding slide as people arrive
  • Good morning, I’m Ivan Mazour the CEO of Ometria, and a very warm welcome to our very first breakfast seminar. Other members of the Ometria team with us this morning include …
  • In the early days of the web, a hit-counter was a sophisticated analytical tool<click> Then, in 2005, along came Google Analytics. It wasn’t the first analytics solution, but there again, the iPhone wasn’t the first smartphone.<click> Google Analytics enabled ecommerce businesses to get huge value out of data – and it did so by providing a technology toolkit, giving people access to meaningful, action-able data AND ALSO thought-leadership – it, for example, <click> introduced the idea of goal funnels and attribution models to the ecommerce world.<click> One of the reasons we set up Ometria is because we believe the Google Analytics growth curve is flattening out – most of the value it has to offer has already been offered. <click> We believe it is time to ‘jump the curve onto a new growth curve - the Integrated Ecommerce Analytics curve. And, just as Google Analytics did before us, we believe that our success is going to be built on two things – great technology, giving access to meaningful and action-able data and thought-leadership – and it is the thought-leadership that brings us here today. Intro Mike …
  • Mike’s first slide
  • Tim Ferris became something of a legend as the creator of the 4-Hour Working Week – however, the story of how he came to live his pretty amazing lifestyle on 4 hours work per week is less well known. Essentially, he analysed his sports supplements business and realised that most of his customers and most of his products were contributing little or nothing to his profits – so he got rid of them.He was running a wholesale business so this was easier than in a retail business but the data is no less valuable
  • Data triangulation - Driving Profit form Data in Ecommerce

    1. 1. Data Triangulation Driving Profit from Data in Ecommerce Welcome to Ometria’s Breakfast Seminar http://www.ometria.com - @OmetriaData
    2. 2. 2 The Ometria Team Ivan Mazour CEO James Dunford Wood COO Dr. Alastair James CTO Edward Gotham Head of Ecommerce Victoria Elizabeth Content Marketing Manager Alexander Gash Business Development Executive http://www.ometria.com - @OmetriaData Djalal Lougouev CFO Tomislav Bucic Business Development Executive
    3. 3. 3 How Ometria Sees Ecommerce Profit Models The value of data Product Models Integrated Ecommerce Analytics Customer Models Attribution Models Goal Funnels 2000 2005 http://www.ometria.com - @OmetriaData 2010 2015 2020
    4. 4. Data Triangulation Driving Profit from Data in Ecommerce Mike Baxter http://www.ometria.com - @OmetriaData
    5. 5. 5 What is Ecommerce Profit ££ Products http://www.ometria.com - @OmetriaData Ecommerce Customers
    6. 6. 6 The Principle of Data Triangulation Profit Products http://www.ometria.com - @OmetriaData Customers
    7. 7. 7 Vilfredo Pareto 1848 – 1923 Born in Paris, Italian national, worked mostly at University of Lausanne in Switzerland 80% of land is owned by 20% of people 80% of peas come from 20% of pods The Pareto Principle / The Law of the Vital Few / The 80:20 rule http://www.ometria.com - @OmetriaData
    8. 8. 8 The Pareto Curve – Long Tail 20% of customers account for 80% of sales Sales 20% of products generate 80% of sales Sales Customers http://www.ometria.com - @OmetriaData Products
    9. 9. 9 Tim Ferriss http://www.ometria.com - @OmetriaData
    10. 10. 10 The Principle of Data Triangulation Most profit Where is the sweet-spot in your business where selling your best products to your best customers generates the most profits? Best products http://www.ometria.com - @OmetriaData Best customers
    11. 11. 11 A Note on Examples & Data    Based on Ecommerce consultancy work over the past 13 years with businesses ranging from High Street brands to 2-person niche pure-plays Using examples from JohnLewis.com – NEVER worked with them so no confidentiality issues – SO hypothetical data But all data, trends and insights based on real examples presented to actual clients http://www.ometria.com - @OmetriaData
    12. 12. 12 The Principle of Data Triangulation Most profit Your best products: 1. 2. 3. 4. Clicked most often Bought most often Highest order revenue Encourage most return visits http://www.ometria.com - @OmetriaData Best products Best customers
    13. 13. 13 The AIDA Model of Customer Journeys Awareness Interest http://www.ometria.com - @OmetriaData Decision Action
    14. 14. 14 The AIDA Model of Customer Journeys Awareness Interest for customers at this stage of their journey … http://www.ometria.com - @OmetriaData Decision Action
    15. 15. 15 Which Products to Promote? http://www.ometria.com - @OmetriaData
    16. 16. 16 Click-Propensity Products Clicked Most Frequently Clicks as % of Impressions 26% 23% 21% 20% 18% 18% 16% 15% 14% 13% http://www.ometria.com - @OmetriaData
    17. 17. 17 Click-Propensity Products Clicked Most Frequently Clicks as % of Impressions 26% 18% Action / Insight 23% 21% 20% Attract new visitors to your site using high click-propensity products 16% http://www.ometria.com - @OmetriaData 15% 14% 18% 13%
    18. 18. 18 The AIDA Model of Customer Journeys Awareness Interest For customers at this stage of their journey … http://www.ometria.com - @OmetriaData Decision Action
    19. 19. 19 Purchase Propensity • 30 products in category (top 12 shown opposite) • Null hypothesis – random-click, random purchase • Within-category, each product = 3.3% CTR, 3.3% purchase • Actual CTR & purchase shown as difference from null-expected http://www.ometria.com - @OmetriaData
    20. 20. 20 Purchase Propensity over-viewed under-sold under-viewed over-sold under-viewed over-sold Number of times product was viewed as % of null-expected Number of times product was bought as % of null-expected http://www.ometria.com - @OmetriaData
    21. 21. 21 Purchase Propensity over-viewed under-sold under-viewed over-sold under-viewed over-sold Action / Insight Adjust traffic flows through your site to match product-views with productpurchase-propensity Number of times product was viewed as % of null-expected Number of times product was bought as % of null-expected http://www.ometria.com - @OmetriaData
    22. 22. 22 The Principle of Data Triangulation Most profit Where is the sweet-spot in your business where selling your best products to your best customers generates the most profits? Best products http://www.ometria.com - @OmetriaData Best customers
    23. 23. 23 Highest Revenue Products 1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th http://www.ometria.com - @OmetriaData
    24. 24. Highest Margin Products low margin highest margin products high returns heavy discount 1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th http://www.ometria.com - @OmetriaData 24
    25. 25. 25 The Principle of Data Triangulation Most profit Your best customers: Best products Best customers http://www.ometria.com - @OmetriaData 1. 2. 3. 4. 5. Have bought recently Buy most frequently Spend most money Recommend you to their friends Leave distinctive data trails on their way to becoming hero customers
    26. 26. 26 Recency, Frequency & Monetary Value The “father of customer analytics” Worked in direct sales (mail-order and ecommerce) for over 30 years Now facilitates the buying and selling of businesses, guided by their customer performance Donald Libey Available free online at http://www.e-rfm.com/Libey/Libeybook2.html http://www.ometria.com - @OmetriaData
    27. 27. 27 Recency, Frequency & Monetary Value  Recency: The freshness of the relationship between your brand and your customer; indicates when customers slip from active to inactive; the primary measure of business vitality  Frequency: The measure of demand; measured in # orders per period of time  Monetary Value: A measure of customer worth; measured as average order value http://www.ometria.com - @OmetriaData
    28. 28. 28 RFM Matrix Recency of last order 0 to 6 months Frequency (orders/year) 6+ 6 to 12 months low low med high low 2 to 5 1st order low low high med low low high http://www.ometria.com - @OmetriaData med high med low Med = £50 to £100 High = over £100 med high Monetary value (average order value) Low = up to £50 high high med med high high med low med 12 months +
    29. 29. 29 RFM Matrix Recency of last order 0 to 6 months Frequency (orders/year) 6+ 2 to 5 1st order 6 to 12 months low low med high med ActionhighInsight / 12 months + low med high RFM analysis can often be distorted by the categories used low recency, frequency and for low low med med med monetary value high high high CHAID can be used to statistically optimise how customers are categorised low low med med low high med http://en.wikipedia.org/wiki/CHAID http://www.ometria.com - @OmetriaData high high Monetary value (average order value) Low = up to £50 Med = £50 to £100 High = over £100
    30. 30. 30 RFM Matrix Recency of last order 0 to 6 months Frequency (orders/year) 6+ 6 to 12 months low low med high low 2 to 5 1st order low low high med low low high http://www.ometria.com - @OmetriaData med high med low med high Heroes-in-waiting – test them with hero-treatment Lapsing heroes – invest to get them back Lapsing – regular attempts to re-activate high high med med high high med low med 12 months + Hero customers – make them feel loved & cherished – turn them into brand ambassadors Lapsed heroes – last-ditch big effort to re-activate Lost cause – try … but don’t hold your breath
    31. 31. 31 RFM Matrix Recency of last order 0 to 6 months Frequency (orders/year) 6+ 6 to 12 months low low med high med 12 months + low med high high Action / Insight 2 to 5 1st order low low med low med Invest different amounts of time and moneymed in the customers that have different value to your high high high business low med low high http://www.ometria.com - @OmetriaData med high low med high Hero customers – make them feel loved & cherished – turn them into brand ambassadors Heroes-in-waiting – test them with hero-treatment Lapsing heroes – invest to get them back Lapsing – regular attempts to re-activate Lapsed heroes – last-ditch big effort to re-activate Lost cause – try … but don’t hold your breath
    32. 32. 32 The Principle of Data Triangulation Most profit Where is the sweet-spot in your business where selling your best products to your best customers generates the most profits? Best products http://www.ometria.com - @OmetriaData Best customers
    33. 33. 33 Different Types of Hero Profile Margin Action Recency: 26 days Frequency: 6.5/yr AOV: £124 Revenue=£806 # discount items=1 Gross margin=£187.5 Regular contact – highlight brand messaging, new products & repeat purchases – added value offers (e.g. gift-wrap) instead of discounts Recency: 33 days Frequency: 8.3/yr AOV: £114 Revenue=£946 # discount items=14 Gross margin=£49.7 Regular contact – highlight bundled offers and cumulative refer-a-friend discounts http://www.ometria.com - @OmetriaData
    34. 34. 34 What is Ecommerce Profit hero products ££ hero customers Right product to the right customer at the right time Products http://www.ometria.com - @OmetriaData Ecommerce Customers
    35. 35. 35 Any Questions? Phone: +44 20 7016 8383 http://www.ometria.com - @OmetriaData Website: http://www.ometria.com Email: info@ometria.com Twitter: Address: 38 Park Street, W1K 2JF @OmetriaData

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