How Incuda builds user journey models with Snowplow

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incuda BI for multi-channel commerce

data-drive your business.

incuda BI for multi-channel merchants

data-drive your business: One better Decision every day © incuda GmbH . 2

we track

behaviour, interest & profitability

of >200 mln. persons

from over 100 countries …

… on a daily basis

Founded 2012, Munich & Düsseldorf. Partners for strategy, business consulting, technology & implementation.

Market focus: E-commerce & multi-channel merchants, international & multi-brand, yearly revenue 20+ mln. €.

Mission: Provide a more mature & powerful BI platform in time of increasing competition.

Agenda


describe an advanced user journey model

see how a journey-based view helps to optimize

- Marketing Performance

- Relationship Marketing

- Product Performance

understand why Snowplow plays a critical role to achieve overall success.

discuss your ideas and questions, ask anytime

Background

Measuring Success: User Journeys


User Journeys are an excelent tool to measure impact of offers and ad items in a

highly parallel marketing environment.

We focus on Journeys which include digital channels, Snowplow is our tool for

collecting information and allows us to build high-quality journeys on a granular

level.

approach description devices channels

Keywords,

banners View/click ad item, Last Click conversionsingle single

Campaigns Response across time and channels single multiple

Journeys Contacts over time and conversion for next order multiple multiple

Cohorts mid-/long-term customer lifetime & value multiple multiple

Why is Journey modelling difficult?


Good customers on average use

− 5 marketing channels,

4 contact devices,

− 1.2 customer IDs,

− 1.3 email addresses.

Over 80% of repeat buyers use more

than 1 contact device.

More than 80% of marketing cost &

journeys are on non-buyers.Number of devices per repeat buyer within 12 months.

Impact on:

- Marketing Performance: different devices used during information / conversion phase

- 360° user view on buyers and non-buyers

- Buying & interest behaviour for different product categories, brands, price ranges

What happens if you are working with raw data?


Raw data experience:

- you see many very short buyer journeys

- mobile journeys with clear product interest but no conversion

- buyers visit at other time of day than non-buyers

Our learnings:

− most products are viewed before ordered, but often not on

the same device.

− People use different devices for information phase (mobile,

work, second-screen) and for orders (desktop, home).

− People use different devices during the day

− Family & friends use the same desktop device (cookie ID)

− cross-device detection happens over time,

requires„automatic“ updates of the user journey (ongoing &

historical data)

Process & Application layers provide interaction data


Campaigns & Offers Touchpoints Customer transaction

Order intake

Revenue

Cancel & Returns

Cogs, Pick & Pack

Raw margin, PC-I

Marketing cost,

Commissions, ..

PC-II

Werbemittel-Detailebene Buyer & Nonbuyer contacts Transactions & Customers

ad media

ID

ad media

ID

transact

ID

transact

ID

…

Profit-driven performance marketing

Conversion; Cost per Offer / Ad itemTraffic acquisition

Cost of traffic, Conversion,

Repeat traffic, …

- Sequence of channels

- Conversion rates

- User Lifecycle

CRM outbound

marketing

many few one

Layers are woven into a „Data Network“: more = higher density


Desktop PC (Cookie)

Mobile

Workplace PC

visit

visit

visit

visit

visit

Email

Call Center

Online-Shop

Customer ID

Order ID,

Product data

Catalog, Ad-ID,

Offline Advertising

Contact data

(email, phone)

Order, Case ID

Email click

Email click

Channels Touchpoints Customer transaction

Tracking tools Channel backend data Data Warehouse / ERP

Subscribe

Log-in

Order

Campaign

Email open Subscribers

call

Case Study: Journey length, based on User Consolidation

Case: established international Online Retailer, comparison based on German shop

− Scenario A: modelling by established Journey agency, based on online data

− Scenario B: enhanced modelling based on online & inhouse data (incuda)


Quantitative findings:

− 50% of Buyer Journeys in scenario A with only

1 or 2 contacts (compared to 32% Journeys B)

− Scenario B shows over 2 times more Journeys

with 7+ contacts

Qualitative findings (based on analysis of examples):

� Journeys show a more consistent approach to

conversion (product information before orders)

� Number of user interactions more in-line with experience of business experts

� Validated responses from outbound communictaion confirmed device usage

Case Study (cont.)

Impact:

� More complete description of decision funnels, better understanding of channel

interaction

� „longer“ journeys provide more opportunities for conversion impulses

� More correct allocation of marketing cost to conversion

Why worked approach B better?

- integration of multiple devices in one journey

- integration of more touchpoints (offline, offsite)

- access to inhouse data

- configuration of technical processes to provide required data

- full utilisation of webtracking platform (tracking, parameterisation, cookie types)


Our Learnings


Integrated Journey modelling impacts:

- Marketing Performance: different devices used during

information / conversion phase

- 360° view on buyers & non-buyers, Lifetime estimates and

opportunities for CRM activities

- Buying behaviour for different product categories, brands,

price ranges

Tracking of contact history should include as many touchpoints

as possible. Owned & offline channels (email, print, co-

operations, etc.) improve metrics for ongoing user activity.

Tracking platform must have all the flexibility which you will

need in the future ; blackbox approach might be risky.

We like Snowplow with the Open Source approach, transparent

source code and architectural flexibility.

How do we do it?

User – Device detection & consolidation

Users can be merged at any time; automated integration of contact history and

user profile data is required for a performant platform management.

Level 1 Secure matching

- Matches devices, accounts and contact addresses through

events and data that clearly identify a user

- Examples: Orders, Email address, log-in to user account

Level 2 High-probability matching

- Matches devices, accounts, contact data, and other information through events and

data that identify the user of the device with a high probability

- Mapping is reversible in case problems are detected

- Examples: Email click-through, contact information and identifiers

Level 3 Post-technical validation

- Confirm or reject matches, based on functional rules (abstraction from technical level)

- Complete control to analyse & overrride single consolidations, DQ checks

- Examples: Manage household users, friends sessions, etc.


campaign

contactcampaign

contact0

User consolidation: examples for consolidation rules

User

Master User

User Response (e.g. Snowplow online visit)

new user devices known user devices

campaign

contacts

user contact

data

log-in to

user account

subscribe to

service / email

referrer = email

click-throughorder (incl.

guest orders)

customer

service request

− initial = 1:1

− Data Load

consolidation

= 1:N

user account

Level 1 consolidation „secure match“Level 2 consolidation „high probability“

− initial = 1:1

− Logical matching rules

− Batch consolidation

on earliest user ID

Post-technical

validationPost-technical

validationFunctional

validationLevel 3 consolidation „post-technical validation“


Our Learnings


User consolidation needs to be 95% precise, otherwise you

cannot use it for personalisation / targeting and you loose a

big part of the business value.

Transparency on single cases and option to change every

consolidation is critical!

Use tagging & cookie features to track across (sub-)

domains and brands. Use custom fields for additional IDs.

At the same time, make sure that you retain a precise view

on transactions (shipping address, email address per order,

etc.). Otherwise you will run into limitations for

transaction mails.

How we build dynamic Attribution curves


When you have the base data in place, calculation of

journeys is straight forward:

- consolidate contact history & group by conversion events

- update journeys every time a user consolidation gets

updated

- Calculate per contact & single admedia item (detail level)

- Take care of system performance

From the feature list:

− user defined curves; bath-tub, dynamic u-curve; channel

boost factors (DTI, TV, …)

− age of contact relative to journey end

− duration between contacts in journey

− onsite engagement

− early timeout predictor for ongoing business

− compare different models, champion challenger approach

Understanding Journey pattern

The structure of Journeys first gives you an

insight into the usage of you contact channels

and ad items.

The sequence of channels used helps to

understand typical acquisition and conversion

scenarios for new users, active customers, top

buyers, etc.:

- Sequence of marketing channels

- Sequence of device types (mobile,

desktop,…)

- Sequence of daytime hours for information

and conversion

On top, the main impact of an offer or ad item

during the AIDA decision funnel is measured:

some offers have a low conversion, but a high

acquisition or re-activation value.


Our Learnings


Journey structure on user base gives you a first

segmentation of users who behave in a mono-like way

(strong preference for contact channel, device, daytime

hour) and other users with more flexible behavior.

Journey structure shows, if information phase and

conversion happen on the same timeline: which users buy

directly, which have a time-related pattern (e.g. buy in the

evening from home, order on weekend)

AIDA modelling gives you additional metrics (activation,

reactivation) for performance tuning.

Integrated Marketing Calendar


A broader integration of touchpoints

leads to better journey data: include all

marketing & sales activities and relate

them to user contacts.

− Add campaign metadata (clear names)

and cost figures.

− Break ad cost down to single contacts.

− Add a meta layer to connect between

cost reports and click-data tagging.

A standardised description of marketing

activities & cost is the basis for

- flexible and automated calculation of

user journeys

- dynamic attribution modelling.

Marketing performance: manage on ad-item level & profitability

Measure financial success for single ad items.

Define success metrics according to your

business model, e.g. net revenue or profit

contribution instead of KUR.

Automate baseline business and push financial

metrics to tools for Autobidding, campaigns,

email, sourcing, ...

Compare metrics between time periods and

understand changes on different levels (traffic,

advertising, visitor structure, product quality,

return rates) and their impact on marketing

performance.


Customer understanding: User Profiling & Segmentation

Profile user segments based on all data available:

- navigation on the shops

- marketing channel and device usage

- product & category interest

- buying patterns, including revenue, returns,

profitability

- geography, gender, age, statistical data on

households, income, education, etc.

Customer Understanding is based on what users buy,

what they are interested in, and what they are not

interested in…

Detailed click data enables data feeds to support

CRM, Lifecycle alerts and Outbound Segment

marketing.


Our Learnings


Design advertising channels in a consistent way:

- inbound vs. outbound marketing

- direct vs. indirect channels (TV)

- cost models (CPM, CPC, CPO, CPL)

Link marketing cost to single contacts, not orders. Key measure is

the cost for a given contact.

Snowplow tracks campaing information on ad-item level; do not

track on aggregated campaigns!

Use CPM, CPC, KUR? Optimise according to your business model!

e.g. net revenue, profit, or your custom metrics.

Make sure your tracking tool supports flexible mapping of click

data information to marketing cost reports.

Understand your users based on what they do (buy, view) and

what they don‘t do. (requires a complete user view!)

Products meet Sales: the “Product Journey”


Different types of products can have different roles:

from building your brand to attracting visitors and

finally converting into revenue, not all products

behave in the same way.

But at the end of the day, product stock should be

sold out or at leas minimised. And higher demand

should be identified as early as possible.

The „product journey“ identifies interest levels, leads

and prospect buyers for individual articles:

- for onsite, offsite, aftersales channels

- for buyers and non-buyers

- for converted and dropped articles

- based on net revenue & profit contribution

Tracking product interest


Product orders are available from backend systems. The major data source for

Product interest data is the tracking tool.

We use Snowplow tracking to gather event detail data

- Product views

- Basket add or remove events

- Basket convert

- Product impressions on home page, overview pages

- Product detail information from filter, search, etc. events on detail level (large

amount of tracking data!)

- other events, like registrations, service requests, …

The flexibility and scalability of snowplow allows us to get all interaction data we

require and add it to the users contact history.

The contact history allows us, to measure effects over time, e.g. out-of-stock &

purchase probability.

Matching click events to marketing effort and conversion


Product profitability is usually

measured on converted orders.

Brands and categories attracting

traffic but with lower conversion are

undervalued with this “last click” logic

(same last click in online marketing ).

The “product journey” maps product-

or service-related events into the

onsite journey and attributes

marketing cost and journey revenue

to each event.

This gives a more balanced view on

the contribution of products and

brands to conversion (category and

brand management).

Weighting of product views within visits (same color).

All events of a visit (same colour in chart)

are weighted according to the attribution

curve (acquisition-based u-curve).

Event types (view, add, buy) are weighted

according to their stage in the conversion

funnel.

Products meet Sales: the User- and the Product Journey


The Product Journey shows you the attributed value each product on your sales channels

contribute to final sales.

Not only Products with good “last-click” behavior are rated highly, but also

- Products, categories and brands which acquire new customers

- Products, categories and brands which generate repeat visitors, e.g. from Email

- Lost opportunities per product and category, linked to user profiles

Marketing campaigns lead the users to your sales channels, use the product journey to

optimize your onsite conversion.

Our Learnings, your Questions


Strategy:

- do Journeys & Attribution properly: if you go „half way“ it will catch up

with you.

- good user consolidation (good enough for targeting) is feasible with

„in-house approach“

- Use Journeys for marketing, but also for CRM, customer service &

product management

Tactics:

- detail traffic & contact data is key for operational excellence; use

flexible tacking, dont use aggregated/blackbox data (GA free)

- channel & device integration is more important than the attribution

curve: if contacts are missing, the curve becomes irrelevant

- journeys must be calculated for buyers & non-buyers. For order events

& non-order events (interest funnel)

Operational excellence:

- calculate attribution scores on all contact levels: user, visit, click-in,

clicks onsite

- allocate marketing cost to contacts, not to orders! (interpretation =

simplification)

Contact us:

Franz Posch

[email protected]

+49 (0)152 / 3363 6683

incuda GmbH . Karlstr. 43/II . D–80333 Munich ..c

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How Incuda builds user journey models with Snowplow

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