In our recent webinar titled "Supercharge Your Revenue Growth Strategy with Knowledge Graphs," we discussed how this unique commercial analytics approach makes understanding multi-dimensional business data more accessible and substantially faster, helping you make smarter decisions and drive Profitable Growth quicker.

Knowledge Graphs are a highly efficient way to represent your otherwise disconnected sales, marketing, customer, competitor, and supply chain data to derive holistic, integrated, and actionable Revenue Growth Analytics insights in minutes vs. days.

Although Knowledge Graphs, like those built with Neo4j, have been around for a while now, outside of core areas like search engines, social networks, and fraud detection, they are not yet prevalent for Revenue Analytics problems. It's an emerging field in Sales & Marketing Analytics (think customer journeys, next-best-sales-action, multi-touch attribution) but sparsely used for other areas like Pricing & Promotion Optimization, Customer Analytics, and Sales Optimization.

Knowledge Graphs naturally excel at storing, visualizing, and analyzing multi-dimensional relationships. They can be compelling analytical assets for any enterprise that has a relatively high purchase frequency and that can track customer touchpoints and engagements somewhere (CRM, marketing data platforms, etc.).

What would typically take layers of nested queries and days or weeks of analyses can be accomplished using purpose-built Knowledge Graphs tailored to the particular industry and company.

Here are some contrasts between traditional descriptive/diagnostic analytics vs. doing a simple query with Knowledge Graphs:

Retail:

How does in-store staff training influence store sales of Product XYZ within 90 days of training?

• Traditional Analytics: You must merge in-store sales from your transactional database with training records and perform some descriptive pre- vs. post-analysis or run a regression / ML model. A multi-step process typically takes a day for a good analytics team to answer.

• Knowledge Graph: a single query can trace the relationship between specific training sessions and subsequent in-store sales of Product XYZ. You can also apply a Machine Learning model quite easily to quantify the impact.

Consumer Products:

What is the typical sales decline when Competitor B launches a product in Category ABC? How does our sales performance change if we counter price promotions with a 10%, 20%, or 30% discount?

• Traditional Analytics: You'd have first to identify competitor product launches from syndicated data, then pull and analyze your sales data during those periods, bifurcated by promotional vs. baseline periods. It's time-consuming and might not account for other influencing factors. Again, a 1-2 day turnaround for most analytics teams.

• Knowledge Graphs: since we can easily map out the relationship between Competitor B's product launches, our sales trends, and our promotional activities (all represented in our graph), we can quickly answer this question.

Of course, the emphasis is building a Knowledge Graph that's "purpose-built" for the particular industry, company, and even domain (i.e., Sales, Marketing, Supply Chain, etc.). Careful design and co-creating with internal stakeholders are paramount as that will solely determine the analytical questions you can quickly answer.

The recording is available here for those who missed the live session or wished to revisit the insights. 

If you have trouble viewing the below presentation, you can also download it here.