In any applied field, analytics or data science is never an end in itself - it is always a means to an end. We don't mine data, we don't even mine insights- data and insights are inputs and "unfinished goods" to our ultimate product-growth.


01. Brand Miner

Syneractiv's end-to-end approach to data-driven omnichannel marketing starts with a Strategic Brand Discovery, which helps us understand objectives, strengths, and challenges in the current go-to-market strategy.

Mission objectives and a logistical project plan are established with the project sponsor and core steering committee.



Voice of Customer & Exploratory Data Analysis

By the time any analytical project has been commissioned, it should have gone through some major vetting of the current or “as-is” state of the project with key stakeholders including interim owners and end-users.

It is critical to have a first-hand understanding of key stakeholder assumptions, perceptions, and expectations and of any gaps in analytical parameters and stakeholder expectations. We accomplish this through a Voice of Customer (VOC) Analysis (a.k.a “stakeholder interviews”) and Exploratory Data Analysis (EDA).

Root Cause & Stakeholder Alignment

VOC & EDA enable us to conduct a root cause analysis and develop initial hypotheses based on stakeholder beliefs that data modeling will either prove or disprove.

At this point initial hypotheses are shared with key stakeholders along with any initial insights from the exploratory data analysis.

This is important because should the data modeling in fact end up rejecting some of these initial hypotheses, this initial discovery and due diligence will be pivotal for stakeholder buy-in.


02. Consumer Drivers

Consumer Drivers is a Deep Learning enabled consumer propensity-based segmentation analysis that segments consumers across their journey to ensure minimum attrition of high value growth segments at each stage. This is a hierarchical micro-segmentation approach that recognizes that the same consumer has different priorities at each stage of the journey and marketing communication and content strategy should be optimized accordingly. We accomplish this by enabling customization of creative, content and touchpoints by segment personas and journey stage attributes.

Pre-Market: Demographic or Psychographic Segmentation (Lifestyle + Attitudes)

In-Market: Need-state or Demand Segmentation (a.k.a Market Structure).

Pre-Campaign: Audience Segmentation

In-Campaign: Remarketing Segmentation (this is a digital extension of Audience Segmentation that takes into account touch-points and site-journeys to optimize Remarketing tactics.

Shopper/Buyer Segmentation: Classifies shoppers based on behaviors such as loyalty vs. switching, recency vs. Frequency.

Value-based Segmentation: Segments customers based on projected value- e.g. Lifetime-value Decile Segmentation.

Priority Audience Clusters

A combination of Machine Learning models is used in Consumer Drivers segmentation methods, including Random Forests, K-Means and Hierarchical Clustering.

These use a combination of your first-part customer data (from your CRM) as well as privacy-compliant 3rd Party segment level audience attribute data. The segmentation output can be directly funneled back to your Customer Data Platform (CDP) or to your campaign activation partner.


03. Demand Miner

Traditional marketing performance measurement relies on a fragmented toolkit that uses a multitude of solutions based on measurement needs and these tools rarely agree with each other. Also, solutions like marketing-mix or multitouch attribution place a heavy focus on explaining results. Then there are pure deep learning models that place their entire focus on predictive power and are minimally concerned about explaining the "why".

ML Enabled Dynamic Model Recalibration

We place equal focus on explanatory and predictive prowess because there’s not much point in explaining history if we are not able to do a reasonable job of anticipating the future.

Demand Miner is a predictive -powerhouse disguised as a campaign measurement solution.

It both identifies internal and external drivers of marketing outcome metrics and predicts market-share outcomes as a result of wargaming competitive scenarios. We leverage a proprietary algorithm that adjusts models in real-time to improve predictive performance.

Syneractiv’s multi-level modeling approach spans audience user-level data from first-party logs or digital cleanrooms, audience creative, network/genre & placement-level data from walled-gardens and Connected TV (CTV) platforms as well as traditional offline media and promotions to deliver a true cookieless single-source and privacy-compliant Omnichannel Attribution Model.

And the model output can be loaded back into cleanrooms like Google Ads Data Hub (ADH) to deliver cookieless remarketing optimization.