Piotr Karwatka's presentation about MarTech at Meet Magento PL 2015

1. 1
MARTECH ENGINE
Marketing Technology
Piotr Karwatka

2. MARTECH – PRACTICAL APPLICATION
2
Client acquisition
• Dashboard for monitoring and
managing communication in paid
media, e.g. Google AdWords,
DoubleClick, Google Shopping,
affiliate networks, aggregators and
price comparison sites, social media;
• Centralized media plan;
• Aggregation of marketing activities;
• Remarketing aggregation;
• Aggregation of a client acquisition
cost (actual cost);
• Combining data from marketing, CRM,
call centers and other off-line sources;
• Antifraud systems;
• A network of dynamic landing pages;
• Unified analytics - connecting tools,
e.g. Google Analytics, Gemius, CMS.
Purchasing retention
• Dashboard for monitoring and
managing communication with clients
in owned media, e.g. e-mail, SMS,
push notification;
• Marketing automation;
• Customer segmentation;
• Product recommendations;
• Loyalty programs;
• Customer scoring (customer
assessment and valuation);
• Unified analytics - connecting tools
e.g. Google Analytics, Gemius, CMS,
system marketing automation.
Direct sales
• Vendor dashboards for managing
communication with clients in
on-line and off-line media;
• Monitoring customer health;
• Cross- and up-selling web/marketing
mechanisms for use by vendors;
• Predefined components for
communicating with customers, e.g.
everyday brochures ready to send;
• Mechanisms of product
recommendation;
• Mechanisms supporting direct sales,
e.g. potential and risk customer
alerts.
CRO/UX automation
• Layout personalization;
• Product recommendations;
• Search engine personalization;
• Navigation personalization;
• Management dashboards for website
personalization.

3. MARTECH – LOGICS
3Source: Hybris
TOUCHPOINTS SITES, APPS, ADS,
E-MAIL, OFF-LINE
ACQUISITION
RETENTION
CRO
DIRECT SALES

4. ACTIONABLE DATA
4
Purchase History
CRM
Cookies (behaviours on www)
GA API
Social Media
SalesManago
Customer’s data:
Sales Data
e-Commerce / ERP / POS
Data Aggregation
Algorithms and Logic
Big Data
+ Reco Engine
Cloudera
Reporting
Personalized
Communication
Dynamic content
Marketing
Automation:
Sales Dashboard

5. MARTECH OPEN ARCHITECTURE
5

6. MARTECH OPEN ARCHITECTURE
6

7. PERSONALIZATION AND/OR MARTECH – DEVELOPING
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Analysis of shopping
habits
Prototype of
personalization
elements
Testing
personalization
prototypes
Designing a dedicated
MarTech solution
Implementation
and integration
Goal – to detect key
purchasing habits, system
constraints and develop the
concept of solution and project
scope.
Realization – workshop, input
data analysis (database
analysis in the areas of trade,
product and customer), IT
systems analysis; preliminary
technical analysis.
The effect of work –
conclusions from the
conducted analyses (used in
marketing, sales, IT and UX)
MarTech and personalization
development plan, a
preliminary plan of MarTech
and personalization
mechanisms application in the
organization.
Goal – to develop the first version
of personalization and Martech
components (segmentation
mechanisms, recommendation
mechanisms, data aggregating and
processing mechanisms) along
with a plan of their use/
implementation.
Realization – creating concept,
mockups, developing prototypes
of mechanisms operating
independently of the current IT
system.
The effect of work – prototypes of
personalization and MarTech
mechanisms and a plan for testing
them.
Goal – to test and optimize
personalization and Martech
prototypes.
Realization – research/testing,
optimizing the mechanisms
(conceptual work, mockups,
developing prototypes of
mechanisms operating
independently of the current IT
system).
The effect of work – tested and
approved prototypes of
personalization and MarTech
mechanisms; revised MarTech
and personalization development
plan.
Goal - to design the final version of
MarTech and personalization
solutions, create mockups, and
the implementation backlog.
Realization – creating final Axure
mockups, preimplementation
analytics,
The effect of work – Axure
mockups, implementation
backlog, planned implementation
analytics (IT and the mechanism
application in the organization).
Goal - implementation of
personalization and Martech
mechanisms, using the gained
knowledge in the current sales
and marketing activities.
Realization - IT implementation
carried out under the strict
supervision of a MarTech
engineer.

8. VENDOR DASHBOARD – ALERTS BY SEGMENT
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Source: http://www.slideshare.net/RetentionGrid/your-retention-marketing-todos-for-each-customer-loyalty-segment/
Potential applications:
• Detecting customers’ potential by
segmentation e.g. frequency of purchase,
the time since the last purchase or
purchase value;
• Preparing and/or automatic delivery of
pre-defined e-mail campaigns, e.g.
win-back campaigns for new customers
who have not got back to a store;
• Detecting promising customer segments,
working on customers using layers: an
increase in purchase frequency,
increasing the purchase value, reducing
the time since the last purchase.

9. CRO – PERSONALIZED LAYOUT
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Potential applications:
• Homepage tailored to the customer's
profile (blocks, offer, navigation,
pop-ups), personalization based on
historical data, e.g. a logged in and not
logged customer and data from external
sources, e.g. Facebook;
• Dynamic website elements (blocks,
pop-ups) appearing depending on the
profile and behavior on the website.

10. VENDOR DASHBOARD – OFFER BY SEGMENT
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Source: http://workingperson.com/, http://www.windsorcircle.com/
Potential applications:
• Automatic preparation and/or sending an
e-mail message containing products and
promotions tailored to customer
segments or individual customers;
• Managing recommendations engine,
taking into account the business logic,
promotions, inventory and marketing
plans.

11. VENDOR DASHBOARD – MARKETING AUTOMATION
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Source: http://www.preact.com/, https://rjmetrics.com/resources/reports/ecommerce-buyer-behavior/
Potential applications:
• Messages sent automatically to the
customer at a pre-planned scenario,
e.g. abandoning the ordering process,
abandoning a shopping cart, abandoned
page (while browsing);
• A sequence of messages welcoming and
introducing the client (onboarding);
• A sequence of messages reactivating or
recovering the client;
• Dedicated offer of the day/week sent
automatically to customers.

12. VENDOR DASHBOARD
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Source: https://canopylabs.com/
Potential applications:
• Specifying up-selling recommendations
(product range, time of transfer
recommendations) directly at the level of
individual clients;
• Specifying preferred format and
frequency of contact by the sales
department;
• Tracking individual user behavior (on-line,
off-line);
• Detecting clients with increased risk of
loosing them.

13. VENDOR DASHBOARD – UP–SELLING ALERT
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Source: http://www.preact.com/, https://rjmetrics.com/resources/reports/ecommerce-buyer-behavior/
Potential applications:
• Detecting customer segments with similar
shopping preferences;
• Detecting clients with specific behavior,
e.g. impulsive shopping, promotion
shopping, purchasing supplemental
stocks of a product;
• Detecting customers interested with the
selected product, product type or kind of
promotion/trigger e.g. a discount coupon
for free delivery.

14. VENDOR DASHBOARD – REORDER/REPLENISHMENT ALERT
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Source: https://www.justrightpetfood.com/
Potential applications:
• Detecting the correlation between the
next purchase and a specific product
(purchase recurrence);
• Developing customer segments that are
willing to renew stocks of a product;
• Automatic preparation and/or sending
e-mails convincing customers to repeat
the purchase.
• Managing the described communication.

15. ACQUISITION – MONITORING COMPETITION ACTIVITIES
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Source: Dealavo
Potential applications:
• Monitoring of prices, offers, promotional
campaigns, the scope of marketing
activities by competition; daily update of
data; alerts.

16. ACQUISITION – DATA AGGREGATION
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Source: Hybris
Potential applications:
• Aggregation and integrating data from
multiple sources, e.g. CRM, Call Center,
Google Analytics, Marketing Automation
system, cash system, marketing tools, etc.;
• Managing a single mediaplan and
purchasing media from one panel (data
integration from internal systems with
marketing tools, e.g. AdServer, Marketing
Automation, affiliate networks);
• Supplementing aggregated data with
external data e.g. demographic or social
profile, data correctness.

17. ACQUISITION – FEED MANAGEMENT
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Source: Lengow
Potential applications:
• Marketing management based on an offer
– emitting product ads (XML);
• Promotion management in the context of
sponsored links, Google Shopping, price
comparison websites, offer aggregators,
affiliate networks, dynamic remarketing
(product presentation), RTB (product
presentation), social media (Facebook
Ads, Pinterest), marketplace (Allegro,
eBay, Amazon and other );
• Managing pricing and promotions policy
from a single panel.

18. CORRELATION ANALYSIS
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Potential applications:
• Detecting product + product correlation,
e.g. most frequently purchased product;
• Detecting correlation between
customers/users;
• Detecting correlation between behavior
on the website (visiting specific sites),
and purchasing;
• Detecting correlations between
stimuli/triggers and purchasing e.g.
customer response to promotions;
• Detecting correlation between th time of
purchase and the scale and type of
purchased products;
• Detecting correlation between repeating
purchase.

19. CORRELATION ANALYSIS
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Potential applications:
• Detecting product + product correlation,
e.g. most frequently purchased product;
• Detecting correlation between
customers/users;
• Detecting correlation between behavior
on the website (visiting specific sites),
and purchasing;
• Detecting correlations between
stimuli/triggers and purchasing e.g.
customer response to promotions;
• Detecting correlation between the time of
purchase and the scale and type of
purchased products;
• Detecting correlation between repeating
purchase.

20. 20
EXAMPLE ANALYSIS

21. PERSONA ANALYSIS
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Potential applications:
• Analysis of shopping habits according to
personas defined on the basis of
interviews and/or testing, e.g. promotion
hunters, gift buyers, thrifty customers,
novelty fans, buyers using
recommendations, etc.
• Analysis of stimuli/triggers in an offer or a
marketing strategy stimulating customers
to action;
• Modeling triggers and a method of
communication (range, scope and
frequency) broken down by individual
personas;
• Combining qualitative and quantitative
research.

22. ANALYSIS OF PROBABILITY
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Potential applications:
• Construction and optimization of
probability models;
• Detecting customers with the highest
likelihood of purchase recurrence;
• Detecting customers most likely to be
lost;
• Detecting breakthroughs in building
customer loyalty, e.g. „Starting the
purchase of product X significantly
increases the chance of being loyal" or
"after the fifth purchase the customer
becomes loyal."

23. SHOPPING SEQUENCE ANALYSIS
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Potential applications:
• Detecting purchase sequence in the
following aspects: product category,
product brand, specific product or cart
size;
• Detecting shopping preferences
depending on the order of purchase.

24. ANALYSIS AND PREDICTION OF CUSTOMER VALUE IN TIME
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Potential applications:
• Analysis and customer segmentation
according to customer value in time
detecting characteristics common to the
most successful clients;
• Predicting customer lifetime value (using
probability analysis);
• Detecting Pareto 20% (the best clients
in terms of purchase value) and aspiring
segments.

25. FIRST PURCHASE ANALYSIS
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Potential applications:
• Analysis of marketing activities (traffic
sources, media, campaigns,
triggers/discounts, season) for generating
new customers;
• Detecting marketing components
responsible for bringing new customers;
• Calculating the cost of acquiring a new
customer;
• Multichannel analysis (taking into
account conversion attribution).

26. TOUCHPOINTS ANALYSIS
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Potential applications:
• Detecting key points of contact with an
offer (website, application, landing pages,
marketing, off-line);
• Modifying UX/marketing so that they lead
customers to the appropriate places on a
website;
• Detecting and removing unwanted
elements in UX/marketing.

27. ANALYSIS AND PREDICTION OF CUSTOMER VALUE IN TIME
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Potential applications:
• Analysis of marketing activities (traffic
sources, media, campaigns,
triggers/discounts, season) for expected
customer value in time, the likelihood of
purchase recurrence and the likelihood of
becoming a loyal customer;
• Detecting marketing components
responsible for bringing the most valuable
customers.

28. THANK YOU! QUESTIONS?
28
Piotr Karwatka (pkarwatka@divante.pl)
Divante Ltd – http://divante.co