10 use cases for Dreamdata’s Data Platform
Dreamdata’s data platform opens a world of opportunity for B2B data and revops teams.
“Data platform?”
Yes. I know we spend a lot of time talking about our Revenue Attribution app. But under the hood, there’s our data-crunching data platform, which opens a world of opportunity for analytics of all colours.
“World of opportunity… How so?” you ask.
Well, our platform performs the functions of customer data collection, extraction, loading, transformation and modelling. All of which is then freely accessible on BigQuery for you to activate with the Dreamdata app or your favourite BI, ML and/or Reverse ETL tool.
What this means in practice, is an end to time-consuming cleaning and transformation tasks, and agonising over the 101 integrations and APIs. Helping data teams get back to the tasks they not only prefer, but that actually add business value.
In this post, we’re going to take a look at 10 ways you can activate your transformed B2B customer data out of BigQuery.
Activating your Dreamdata data starts with BigQuery
Before we jump on the use cases, let’s take a quick look at how to connect to your Dreamdata data in BigQuery.
Connecting to your BigQuery data is a simple two-step process that looks like this 👇
Yup, it really is that simple.
And with it, you now have access to the same tables and data models we use to power the Dreamdata app!
At its core, this means that data teams avoid the hassle of having to work through the data; deduping, standardising, joining, etc. It also saves you time on those pesky integrations, so the commercial teams can use the best tools out there at the switch of a button (and some $ of course).
Dreamdata Lead Data Scientist, Mikkel Settnes, covers the basics of the platform in greater detail here. You might also be interested in this post on the 6 most common causes of dirty CRM data.
*Oh, and if you don’t yet have a Google Cloud account, give us a shout. As a Google Cloud partner, we’re able to get you $500 in credits. More here.*
With the transformed data then sitting in BigQuery, you’ll be able to:
Run your own queries on our data models
Copy, manipulate, join and use the data within BigQuery
Connect your favourite BI, ML and Operational Analytics tools to the Bigquery data. These include: Hightouch, Google Data Studio, Tableau, PowerBI, Sisense, Looker, and Metabase.
Check out our latest destinations and dashboards here.
All of which might, in some way or other, aid you in running our top 10 use cases 👇
10 use cases for Dreamdata’s Data Platform
1. Lead scoring
Lead scoring continues to play a prominent role in B2B sales. But Sales and Marketing teams are eager to get the manual task off their plate and focus on their respective efforts to attract and close deals.
Dreamdata tracks and identifies accounts from the minute they first engage with your company. As the customer profile gets enriched, you can set up automated scoring based on characteristics and behaviours indicating high intent. This means that you can move away from the manual processing of leads and towards automation.
What’s more, with the structured data, you can jump straight into predictive lead scoring algorithms. More on this in the ‘predictive analytics’ point below.
To close the circle, you can then use Reverse ETL tools to feed these reports back into your CRM, ad platforms or automation tools - also more on this below.
2. Audience building
Marketing teams often need highly segmented audience reports to build personalised retargeting campaigns, etc. These reports typically draw on account behaviour, which can only be gained with thorough end-to-end tracking.
Dreamdata’s tracking and transformation makes audience querying not only possible but simple.
An example query for building an audience consisting of: all users from companies (accounts) in your SQL (Sales Qualified Leads) pipeline, that visited the pricing page within the last 30 days, would look like this 👇
Before moving on, it’s worth doubling down on the word ‘account’ here. Dreamdata’s data platform collects, transforms and models the data exclusively for the B2B segment. Meaning that, unlike B2C transformation tools, the data is joined at the account level (into companies).
3. Improving cross and up-selling
Product teams are keen to have their offerings pushed to customers. The success of these efforts often relies on the precision of Sales and Marketing teams’ timing. Which itself relies overwhelmingly on experience, intuition and a touch of good fortune.
The data sitting in BigQuery can transform this art into a science.
At the most basic level, historic data can help identify commonalities in characteristics or behaviour when cross and up-selling has previously worked. Flags can then be implemented to highlight when customers meet these criteria or trigger events moving forward.
This process can serve to improve personalisation on sales outreach and retargeting ads.
4. Enrich CRM and automation tools through reverse ETL
The advent of reverse ETL has set Marketing and Sales teams towards the ambition of getting relevant data from their warehouse back into their tools.
“Reverse ETL is the process of copying data from a cloud data warehouse to operational systems of record, including but not limited to SaaS tools used for growth, marketing, sales and support.” - Tejas Manohar, CEO of Hightouch
But what if the data sitting in the warehouse is dirty in the first place? The whole process is jeopardised.
Dreamdata provides the clean, unified customer data in your warehouse, and as such the necessary trust in the data. This modelled data can then be synced to your CRM, ad platforms, etc. using reverse ETL tools, much more reliably.
5. Predictive Analytics
Being able to improve acquisition, forecasting, churn, and sales decision-making by predicting outcomes is a growing desire in the B2B c-suite.
The ML prediction models require large amounts of structured data to both identify patterns and characteristics, and to achieve the necessary level of accuracy and reliability.
Dreamdata’s data transformation and modelling delivers this structured data so that B2B data teams have everything they need to implement predictive ML models.
6. Tailor price accuracy
Price invariably plays an important role in pushing deals over the line. The customer’s requirements, the competitive landscape, budget, risk of churn, revenue and deal targets all have to be weighed up when arriving at a price.
Again here B2B Sales teams depend on their experience and intuition to make decisions on pricing. And again, leaders are looking towards automation.
However, structured historic data offers insight into prices and customer data, which can give the teams the upper hand.
7. Reduce churn
Customer retention is another crucial business objective. Picking up signals and trends for when customers are likely to churn has long been an art of Customer Success teams.
Here too data teams are being brought in to implement ML algorithms that can scrutinise the customer data to find patterns and common characteristics of those customers who decide not to buy or stop using the product.
Predictive analytics can then be run to help predict customers that risk churning. And once more, reverse ETL tools can be used to send this data back into your Customer Success tools.
All of which is enabled by the reliability of the structured data sitting in your Dreamdata BigQuery.
8. Calculate Customer LTV
(credit: clevertap)
Customer LTV has now positioned itself as a key B2B metric. Arriving at an accurate measure requires lifetime tracking of B2B customer and revenue data.
With Dreamdata’s unified data warehouse, you get all the costs that have been associated with acquiring customers and the recurring revenue customers are bringing throughout their lifetime.
9. Discover content performance
With content marketing showing no signs of decline, CMO’s are keen to get accurate insights into content performance.
Dreamdata tracks and collects all touches on the website, whenever these take place. The data modelling then connects this all to revenue at pipeline through attribution algorithms. All of which can be easily queried.
This means marketing managers will learn how and when e-books, blog posts or other content types are impacting pipeline and revenue. And in this way help optimise content strategies.
10. Performance-based and commission-based pay
Performance-based and commission-based pay is, as we all know, commonplace in Sales and Marketing circles. And is entirely dependent on proving the success of specific activity.
The reliability of the collected and transformed data on Dreamdata’s BigQuery, means that you can pull reports on attributed first-touch to allocate pay or bonuses to teams or individuals.
Check out our intro to Data Platform video —>
Conclusion: a word on resources
It’s clear, that the use cases vary (drastically) in their complexity and objective. The decision of which you should implement to meet your particular needs must depend on your level of data maturity and the availability of resources.
While the transformed data makes querying a lot simpler (we show a couple of examples below) you need to adapt your uses - and ambitions - to the potential value these will bring versus the cost/effort involved in implementing these.
Of course, you might already have the requirements set out. In which case, it might be easier to reach out to the Dreamdata team for a more tailored conversation about how Dreamdata might help you build and execute the use case.
Finally, it’s worth reminding readers that Dreamdata’s app provides a wide range of analytics and attribution dashboards out-of-the-box. No-code models that offer immense value to any B2B’s revenue activities.
For more on what analytics you can perform on Dreamdata’s app head over to our product videos, or simply see for yourself👇
