New mobile apps to keep an eye on
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What new social media mobile apps are available in 2023?
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Use new social media apps as marketing funnels
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Try out Twitter Spaces or Clubhouse on iPhone
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What app are you currently experimenting on?
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Predictive analytics and AI models are opening up new opportunities for B2B SaaS companies to improve their marketing performance.
By tapping into machine learning, you can uncover insights that augment your marketing efforts and help exceed KPIs.
However, implementing predictive AI for marketing requires a thoughtful, phased approach.
You can't just plug in off-the-shelf software and expect immediate results.
It requires laying the right data foundation, focused testing, and integrating insights across your workflows.
In this blog post, we outline a step-by-step process for successfully leveraging predictive analytics in B2B SaaS marketing:
1. Start by Defining Goals and Focus Areas
First, be very clear about your specific marketing objectives.
Where do you want to move the needle - is it improving lead conversion rates? Improving lead-to-meeting conversion rates?
With well-defined goals, you can focus your modeling efforts on the highest-value opportunities.
No need to boil the ocean early on. Pick targeted areas where predictive insights can have an outsized business impact.
2. Prepare Your Data Foundation
Just like any machine learning application, your algorithms are only as good as the data you feed them.
But how do you ensure the quality of your data is sufficient?
The illustration below highlights key data sets you would need, to make accurate predictions.
The data type depends on the customer lifecycle stage you’d like to predict for (e.g., acquisition, PLG, retention).
Let’s get more specific.
If you try to predict for the acquisition stage, your contacts (new inbound and outbound leads) should contain more data like lead source, firmographics, demographics, and activity.
No matter which marketing automation tool you use (Marketo, Pardot, HubSpot), for optimal results your data should match the flow in this chart:
But regardless of the user journey stage you’d like to score, you want to make sure any data in your marketing and sales stack is structured and cleaned.
Data that is enriched, well-organized, and free of errors will significantly increase the accuracy of your predictions:
- CRM (preferably enriched) - Your CRM contains key details on leads and accounts such as titles, company size, industry, etc. Enriching this with additional demographic and firmographic data can uncover attributes that correlate with conversion, churn, or upsell propensity. These data points allow models to segment and predict behavior.
- Marketing automation - This system tracks prospect and customer journeys across email, forms, landing pages, and other touch points. Event logs of marketing engagement can reveal patterns associated with outcomes. Sequences leading to conversions can better inform your predictive model.
- Web analytics - Granular web behavioral data is hugely valuable for predictions. Pages visited, content downloads, site search terms, and other analytics provide indicators of buyer intent. Models can connect web activity to conversion rates.
- Product usage data - For SaaS firms offering free trials or freemium versions, product usage patterns can factor into buying propensity predictions. Usage frequency, feature adoption, and in-product behavior provide leading indicators of retention or churn risk.
Keep in mind, your CRM data forms the core, but behavioral and usage data add richness to uncover relationships between attributes and outcomes.
The more relevant data you have, from across the marketing and product stack, the better your predictive model can find signal, amidst the noise.
3. Run Small Pilot Projects
Don't try to tackle your most ambitious predictive initiatives right away.
Start with bite-sized experiments focused on specific challenges.
For example, you might want to target a specific stage in your acquisition funnel, like predicting which new lead will become a sales accepted lead (SAL).
Monitor the pilot results closely and be ready to tweak the data inputs and algorithms accordingly.
Refine your approach before scaling up and embedding predictions across all your marketing workflows.
4. Expand Your Analytics Talent
To make the most of predictive AI, you'll need the right mix of technical and marketing skills.
If you use a no-code solution like Forwrd, which is designed for non-technical business leaders, you should be able to handle the entire model building on your own.
However, if you aren’t using a solution like Forwrd, consider bringing on data scientists well-versed in machine learning alongside analysts who understand marketing ops.
5. Focus on Augmenting Marketers
Be sure to position this predictive analytics initiative as a way to empower your human marketers with better insights - not replacing them!
The goal should be integrating data-driven recommendations across campaigns and workflows to help staff work smarter and faster.
Find the right balance between automation and human oversight.
6. Maintain Rigorous Testing
Even after initial deployment, you must continually monitor and verify your model’s performance against actual results and legacy approaches.
Don't put blind faith in predictive models. Expect to test your predictive insights versus your old method and be ready to retrain algorithms on new data, if your algorithms are not self-learning.
By taking this phased, iterative approach, your B2B marketing team can tap into the upside of AI while mitigating the risks.
Follow these steps to set your predictive analytics initiatives up for success.
The payoff for MarTech leaders will be gaining superior visibility into prospects, being better positioned to serve them in a personalized fashion, and exceeding performance KPIs.
