This document discusses strategies for engaging B2B consumers. It notes that B2B buyers now research online like consumers, so B2B companies need to blend B2C and B2B engagement approaches. Specifically, companies should target the right audience by using consumer data to understand behaviors and interests. Two examples are provided where companies successfully drove desired behaviors and outcomes through targeted campaigns that creatively engaged specific audience segments.
2. Agenda
Traditional engagement strategies
Targeting the UK business population
The emergence of the Prosumer
The benefits of blending
Choosing and matching the right consumer data
Real world examples
Driving the creative and media mix
Rewarding behaviour
Summary
3. B2b sales engagement stack
High Value
Corporate
• Senior level engagement
• Business-wide solutions
Enterprise
• Account management
• Single department
SME/SoHo
• Marketing lead engagement
Low Value • Tele-supported
6. B2c blending
Such a large market segment needs effective targeting
Adds a new layer of knowledge
Help inform social media strategy
SoHo/SME purchases not always commercially driven
7. The emergence of the Prosumer
Technology is driving a new decision making process
A threat
Buyers research 10.8 reference sources
79% of buyers use their smartphone to reference products &
services
54% comparison shop
Sales engagement is becoming less relevant
Loss of brand control
Reference: Google ZMOT research
8.
9. The emergence of the Prosumer
Technology is driving a new decision making process
An opportunity
Opportunity to listen and engage early
Nurture prospects
Multiple channel engagement
New ways to transact
Reference: Google ZMOT research
10.
11. Choosing the right consumer data
• Off-the-shelf robust solution
Geo-dems • Postcode level can provide misleading
outcomes
• Household and individual level data
Lifestyle • Good insight into household make-up and
interests
• Ideal for trigger based products/services
Transactional • Ensure sufficient coverage to develop
robust model
12. Define the outcome
An effective model needs a clearly defined objective
Audit your existing database
Remove non-marketable records and PAF check
Match criteria
Agree type of match i.e. postcode, household, or individual
Test model
Ensure match levels are sufficient to produce a robust model
Run test campaigns
13. Campaign
deployment
Creative Driving behaviour
Van Insurance Loyalty
14. Commercial Insurance – Van drivers
Key Cluster Demographics
Household income £30,000-
£40,000
Trade/craftsman
Average credit risk
Pay for insurance in full
Interests include fishing,
football and camping
Subscribes to Sky
More likely to read The Mirror,
Daily Star or Sun on a daily
basis
17. Rewarding behaviour – Trade Wholesalers
Key Cluster Demographics
Home owner
Married with two dependants
under 10
Above average credit risk
Interests include betting,
bingo, days out, cinema &
football
More likely to read The
People Sunday Express
Typical sales engagement – marketing needed at lower level
Challenge biggest market lack of info 1-10,11-250,250-999, 1000
Selection criteria high low – Corp and Enterprise covered, SME/SoHo lack of data, insight and low ROI BIGGEST MARKET LEAST COVERAGE
Set the scene to drive new communication that encompasses social drive decisions – clarity
Later walk through a number of examples. Consider types of data and its use
Set the scene to drive new communication that encompasses social drive decisions – clarity Define the outcome Agree matching criteria i.e. individual, household or postcode level Test data sources for match levels to your own file – low level matching will lead to poor model