Connecting Disparate Data for Customer Journey Insights

Modelling the Customer Journey Using SEM

Key Objectives & Approach

1. Connect multiple data sources (research, media spend, social media and retails sales) to understand

causal relationships.

2. Can we understand the journey of offline media versus digital media?

3. What place do social media display banners play in the wider insight eco-system compared with the

master-brand and category brand Semantic Engagement IndexTM (a social consumer engagement

metric developed using Linguistics – slide 5)

4. Is there any salient concepts we can use to help us drive online conversations about brand Alpha?

Our approach to understanding this involved developing Structural Equation Model (SEM) – a method

commonly used in social science to connect disparate statistical models and gauge the importance of

latent (unobserved salient) concepts. We created a path model to display the results.

Structural Equation Modelling Correlation is significant at the 0.01 level (2-tailed) Correlation is significant at the 0.05 level (2-tailed)

Consumer engagement as a lower funnel metric within the brand Alpha insight ecosystem

TOTAL COMMS AWARENESS BRAND

ALPHA

BRAND ALPHA MASTERBRAND NET

POS SEITM

BRAND ALPHA RETAIL SALES

VOLUME

0.56

BRAND ALPHA ENGAGED SOCIAL MEDIA VOLUME

0.25

0.21

INSTORE

TV

DIGITAL DISPLAY

TOTAL BRAND AWARENESS

0.15 0.33 0.201

BRAND ALPHA NET POS SEITM

0.45

CARES FOR YOU

MAKES YOU HEALTHY

WOULD RECOMMEND TO

OTHERS

0.21

0.34 0.40

0.16

RADIO

SOCIAL MEDIA DISPLAY

PRINT

~ 1 Week Lead

0.05

OUT OF HOME

Causal relationship

Co-varying relationship

Brand Alpha Media Spend

Brand Alpha Tracking (% or % endorsement)

Net Positive Semantic Engagement and Engaged Volume

Retail Sales Volume for brand Alpha LEAVES YOU SOFT & SMOOTH

The Role of SEITM within the brand Alpha Insight Ecosystem

Key Insights

1. Offline media transmits through to positive consumer chatter via awareness channels.

2. Digital display is strongly associated with consumer engaged volume for brand Alpha on social

media.

3. Positive social chatter about brand Alpha is strongly linked with agreement on intrinsic brand

Alpha attributes (including likelihood to recommend) from survey based tracking.

4. The net SEITM (positively engaged consumer chatter) is positioned closer to retail sales as

opposed to social media display which is more of an upper funnel metric, driving brand

awareness.

About Us

The Semantic Engagement Index (SEITM) is a product of Stance-Shift AnalysisTM*. Published and peer

reviewed, Stance Shift AnalysisTM reveals what really matters to the consumer. This is the underpinning of

our approach to measuring social media commentary. Stance-Shift measures consumers’ verbal shifts in

positioning as they talk, where the “shift” infers a landmark change in emotion, intensity, appraisal and

commitment towards a subject (brand, topic, campaign or concept)

This approach enables us to solve for what others miss: Size, Trend and New Concepts.

Two levels of scoring:

1) Emotion and Commitment is understood through engagement scoring – far superior to simple words/comment frequency commonly used to create “sentiment metrics”.

2) Adaptive tonality scoring (the + and -) to understand negative and positive sentiment.

The Semantic Engagement Index SEITM integrates our Stance Shift measurement engine to power

consumer insights and advanced analytical modeling.

The Semantic Engagement IndexTM

Stance Analysis: Social cues and attitudes in online interaction, Mason, et al, Linguistic Insights

Michael Wolfe CEO Bottom Line Analytics Global E: mjw@bottomlineanalytics.com M: 770.485.0270 www.bottomlineanalytics.com

Masood Akhtar Managing Partner, (EMEA) Bottom Line Analytics Global E: ma@bottomlineanalytics.com M: +44 7970 789 663 www.bottomlineanalytics.com