The increasing complexity of digital landscape on one side and huge business expectations on the other side are the driving force of change in e-commerce. Fueled by tons of data machine learning and artificial intelligence are slowly becoming the norm. But algorithms themselves won't be able to change the companies and deliver success. Entire companies need to change as well. How to embrace this change? Where to start and what to expect? How to organize yourselves? We'll deep-dive into data-driven digital marketing framework, followed by insights and case studies from clients and finish up with a stack of tools and takeaways you can use to produce some quick wins.
5. Media consumption vs spend 2016
eMarketer 4/16 & IAB 2016
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Print Radio TV Desktop internet Mobile internet
Time Spent Ad Spend
6. 3 things that will redefine your digital focus
BUDGETS WILL
SHIFT TO
ONLINE
BUILD
AWARENESS
AND INTENT
ONLINE
DELIVER BETTER
ROI
7. TO SUSTAIN THESE SHIFTS COMPANIES WILL RELY ON
DATA TO GROW AND OPTIMIZE THEIR BUSINESSES
16. AI is not a plug-in
Enhance
Scale
Automate
Build processes manually
17. 4 pillars of AI future
Data Technology Process People
18. 4 pillars of AI future
Data Technology Process People
19. The problem of No Data vs the problem of Big Data
TRADITIONAL RETAIL VS ECOMMERCE
20. Entry barriers offline are
much higher than online
Entry barriers
Number of competitors
Number of competitors is rapidly
increasing
Growth of online user
population is slowing down
Number of users
Returning customers
Number of returning customers
is in decline as new competitors
are entering the market
Cost of advertising is rising and
one-time customers are not
profitable anymore
One-time customers
Main difference between offline and online retail
21. One-time customer revenue stream
CPA
GM CPA <= GM
The only way your business could be profitable is achieving CPA
much lower then your Gross Margin.
22. One-time customer revenue stream
CPA
GM CPA > GM
Due to increased competition CPCs are increasing and CRs are
decreasing.
23. Shift to retention model
CAC
GM
CLV <= CLC
Sum of GM from all customer transactions should be higher then
sum of all costs connected to the customer
GM GM
GM
GM
GM
MRC MRC MRC MRC MRC MRC MRC MRC MRC MRC MRC
CLV
CLC
24. CLVP = (P × AOV) × AGM) × ALT
The tricky part is calculating your CLV
35. Gather and prepare the data
Transactional data from
client‘s ERP
3rd party data for
enrichment
Behavioral and
demographic data from
Google Analytics
36.
37.
38.
39. Ecommerce segmentation
K-means clustering based on RFV
Create distribution for new features like
ALT
Add them to clusters to create more
acurate segments
Clustering:
2
3
1
High value
High Frequency
Grey zone Home run
Grey zoneDead zone
41. 2 main usecases of AI
Insights
Use AI to deliver better and deeper
actionable insights for your business.
User experience
Use AI to deliver better experience to
users, automate processes and scale
the business.
42. Customer clusters
segmented by purchase
behavior, CES & LTV Size of clusters
How are your customers
segmented?
Roadmap to ML in Ecommerce
Prevent churn
and re-engage users
based on deviation
in their behavior
Predict LTV
of newly acquired users and
bid accordingly
Customer
transitions
Between clusters
SaaS Ecomm metrics
like churn, retention, ALT, CES
46. 2 areas that we need to focus on for growth in 2018 and beyond
EXCEPTIONAL
LIFETIME
EXPERIENCE
BETTER
PERFORMANCE
AND ROI
47. Thank you
01 02 03 04
Email
andraz@red-orbit.com
Twitter
@andrazstalec
LinkedIn
Andraz Stalec
Web
www.red-orbit.com
Red Orbit, Jožeta Jame 12,1000 Ljubljana, Slovenia
W: www.red-orbit.com |E: info@red-orbit.com | T: +386 (0)590 75 680
Editor's Notes
We live in a tremendous time
…
yet, retailers find it harder to sustain growth & profitability
Source: https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/
Our world is becoming fully digitalized
Consumers are moving from offline to online
Entering the 3rd phase of digital tranfsormation:
1st = making information accessible
2nd = mobile & social media + how they connect people
https://www.youtube.com/watch?time_continue=31&v=shDMy232MAA
3rd = world of AI
Leading companies moving from mobile first to AI first strategy
Road to AI ni implementacija AI toolov ampak
postaviti sistem (procese) na roke (manual) in
jih potem avtomatizirati in izboljšati z AIjem;
tako kot postavljaš GH in potem avtomatiziraš
Enter the AI era:
There are 4 pillars of AI future
With explosion of data, online advertising and ecommerce are becoming increasingly measureable and actionable.
eCommerce, like traditional retail involves taking in a lot of data and trying to make the best decisions based on the data that you have. In a retail store data is relatively limited, you might know how many customers come in the store each day (or at least be able to estimate it), but it’s hard to keep track of how many customers try things on, how long the average customer stays in a store, or how many items they look at during each visit.
Online, it‘s much easier to track customer metrics because the data is easily accessible. You can tell exactly how long the average person spends on your site, which items they look at, what they add to their cart, and what they end up buying in the end. Unlike brick-and-mortar retail where the complexity comes in when you’re trying to collect data, online the complexity is making sense of all the data this is coming in.
Need to shift to retention model, where customer profitability is a key metric
CLV = SUM of all GMs
CLC = CAC + MRC
CAC
MRC = cost of running business – servers, licences, client retention, staff
One key component is keeping MRC as low as possible -> renention should not depend on advertising
P = average monthly purchases
AOV = Average Order Value
AGM = Average Gross Margin
ALT = Average LifeTime in months
Steep function
Create clusters using RFV or AOV and Freq
Add ALT so you get segmnets based on LTV