Unique data can drive differentiation and unlock competitive advantages for businesses today. Many organizations struggle with finding the right data among the disparate data providers and marketplace ecosystem.
In marketing, the strategy always needs to be data-driven. The B2B marketing landscape is constantly evolving, making it hard to make accurate predictions and forecasts based on historical, internal data. As organizations struggle to expand and scale their data sourcing capabilities, new technologies have been developed to overcome many of these challenges with discovering, sourcing, and purchasing external, and ultimately integrating it with internal data.
At the same time, trying to get the right external data is an expensive venture, with about a quarter of annual spend going towards sourcing data—all to generate insight into business performance and revenue.
This blog will discuss how to make it easier to strategically source external data, and how to get the best insights, create value with your products, and the right experiences for your customers.
B2B marketing challenges
Today, it is common practice to use AI, and machine learning predictive models to track customer and market trends. Even though that these technologies are now more mature, businesses still need to be able to trust that the models they build are accurate, perform properly, and can scale effectively.
A recent survey from an independent research firm showed companies with an annual revenue of over $250 million spend about 27% of their marketing operations budget on data. The amount of available data to choose from when it comes to sourcing external data creates several challenges. These are the top three:
- Brand perception does not reflect the desired corporate identity or business strategy. How do you create a brand, and build awareness among your target audiences? It is difficult to understand new target audiences while relying on the data that exists within the four walls of your organization. If you want to get a better understanding of prospects, you need external data. Yet, it is challenging to understand what types of external data points would actually help you not only understand new audiences, but also enable you to build a presence on the channels that your audiences interact with.?
- Quality of customer or market data. Is your data fresh, of high quality, and keeping pace with the market?
- Changing economic conditions. Rapidly changing market environments affect how a business operates. COVID-19 is a perfect example in demonstrating just how quickly predictive models (trained on historical data) become obsolete.
For the data and analytics professionals on marketing teams, having to deal with large volumes of data opens up further challenges including difficulties processing and formatting big data quickly enough to act on it and get the insights before market conditions change again..
External data challenges
Sourcing external data is a complex, tedious, and risky process. There are too many sources and providers, and often, companies don’t know what type of data to look for. The data purchasing stage can also involve legal negotiations which can take months to resolve.
Even after buying datasets, the raw data cannot be used right away to enrich and boost the accuracy of predictive models. There’s a long process of cleansing , matching, and integrating external data with existing internal datasets—often done manually by data scientists or data engineers..
After all this is done, there’s no guarantee that any of your data purchases will provide an ROI. Additionally, data collected from public sources get stale quickly and pose compliance risks.
These days, the use of external data is becoming more of a necessity for B2B marketing. . Organizations are very aware of the competitive edge they can gain by incorporating external data into their data strategy and business models.
As insight maturity increases, external data sourcing grows, but having the right partners really makes a difference. Nearly half of advanced insight-driven businesses still engage data brokers to hunt for data, while also tapping into data marketplaces to source new data.
Data marketplaces facilitate the buying and selling of datasets from several different sources, but there are limitations to sourcing external data this way.
On the other hand, external data platforms offer a differentiating factor by providing an all-in-one platform for accessing all the relevant external data sources, understanding their impact on data analytics and predictive models, integrating them with internal data, and deploying more accurate predictive models.
|Data Marketplace||External Data Platforms|
|An online store that facilitates the buying and selling of datasets from different sources||Provides access to all relevant external sources across different data providers in one place|
|Provide a more public form of data sharing,and a platform to locate required datasets. (But you must know exactly what you are looking for)||Helps you understand which data signals you need and the ROI they will drive|
|Only provides data access, and does not guarantee desired business outcomes||Simplifies and streamlines the data sourcing and purchasing process|
|Data quality is not always guaranteed. It is not always clear as to how often datasets are updated.||High quality, frequently updated, and compliant data guaranteed.|
|Facilitates data purchasing but does not help with the steps required before and after such as data matching and integration||Helps with every step of the data sourcing process, from discovery, purchasing, preparation, integration, model training, compliance, deployment, and retraining|
|Cost of purchasing different commercial datasets can add up||Pricing of the platform is cheaper that purchasing multiple datasets from different providers.|
How Explorium enables higher conversion rates
A leading marketing firm already had a method for building their lead scoring models. They relied heavily on their own internal data such as ad clicks, engagement data, and product views on their website.
Relying on their internal data limited their effectiveness in converting leads to customers, because they didn’t have enough context to fully understand their target prospects. They approached Explorium to help them create a new lead scoring model. By combining their internal data with several external sources from Explorium’s Enrichment Catalog, they were able to come up with new indicators, such as:
- Spending potential and financial stability metrics
- Search engine queries in related fields
- Number of previous purchases in the same category
- Demographic data
After enriching their internal data with Explorium, they were able to build more accurate lead scoring models. The company improved their conversions by 18%.
Explorium offers an end-to-end external data platform for advanced analytics and machine learning. Our unique platform automatically discovers the most relevant external data sources and signals, and integrates them with internal enterprise data to enhance predictive models.
Watch out most recent webinar and learn how to find a competitive edge with an external data platform.
1. Craig Moore, “Tracking the True Costs of B2B Marketing 2020”, Forrester, Oct 5, 2020. https://www.forrester.com/report/tracking-the-true-costs-of-b2b-marketing-2020/RES171586