The Buyer’s Guide to Artificial Intelligence Software For Sales
January 24, 2017
This article was originally published on the Hubspot blog by Sean Zinsmeister, Vice President of Product Marketing at Infer.
Salespeople have never had so much technology at their fingertips. Some of the latest — and possibly most promising — tools for sales teams use predictive analytics, a form of artificial intelligence technology that can optimize decision making around sales efforts. But with all the products promising to tell the future, it’s hard to discern which can actually deliver.
Top players like Salesforce and Microsoft have rolled out AI-driven tools over the last year. Investment in AI startups is at an all-time high. This type of software uses techniques that gathers customer and prospect data from multiple sources, runs it through machine learning models to predict which leads are most likely to convert, and present the findings to Sales in the form of top-scoring prospects and accounts.
So how do you know which tool, if any, is the best bet for meeting your sales goals?
What to Look For in a Sales AI Vendor
Predictive analytics for Sales truly can change the way you run your sales operation. AI-driven software can eliminate a great deal of manual work, helping you make decisions on how to approach prospects, personalize your conversations, and most importantly, focus on the leads that deserve extensive follow-up. For initiatives like moving up-market or adopting an account-based strategy, AI might be the only scalable way to do it.
There a few aspects which each vendor should be evaluated on to ensure you receive a strong return from your investment. Vet each vendor with the following five criteria before making a purchase.
1) Built by people who know the industry
Just because a predictive vendor has an internal data science team doesn’t mean they know how to build models for your particular field. That’s because predictive platforms aren’t just about the math — they involve decisions about what signals or data sources to include, which predictions are most useful, and how companies will actually implement the platform’s insights.
2) Experience deploying in all types of environments
Talk to potential vendors about how they have deployed solutions in the past and whether some of those situations mirror your own. Discuss your company size, how your activity aligns with those of other internal teams, and which applications you use on a daily basis. The best platforms should be easy to add to your already established workflows regardless of which part of the org chart you represent.
3) Transparency about data sources and signals
You won’t be privy to the intricate details of how your predictions were calculated, so you need a great deal of trust in your vendor before taking action on predictive insights. This is the “last mile” problem of AI, and it can stop you from truly adopting a predictive-driven approach to sales. Look for a vendor that uses a diverse group of internal and external signals and is eager to share how they build predictive models for each customer.
4) Reliable integrations with other sales and marketing tools
When you adopt predictive, you’re asking your sales team to buy into a new, AI-driven approach to their jobs. So the more you can choose a product that’s sticky — meaning it integrates smoothly into existing workflows using data they already trust — the more immediate value you can derive from your investment. Assess your vendor’s list of integrations, making sure it includes every major sales and marketing platform, uses open APIs whenever possible, and is expected to continue growing.
5) Ability to scale as your company grows
Predictive scoring models must be improved over time as your company acquires new data and grows in size. Discuss how your potential platform scales, including the way models are updated and how often. Good vendors will personalize models for each customer, monitor their performance, and carefully retrain them when the timing is right. If a vendor has the same approach for every customer, it might be time to look elsewhere.
5 AI Use Cases for Sales Teams
Predictive intelligence for sales helps you make crucial decisions about your company’s growth. It improves major KPIs like revenue, growth, win rate, annual contract value, customer acquisition costs, and a slew of others that are all affected by how well you approach and close new business.
Here are a few examples of what predictive is capable of today:
- Identifying your ideal customers and finding prospects that match those profiles
- Scoring leads by both fit and behavior, showing which are most likely to convert
- Driving expansion, like moving up-market to pursue bigger customers or assessing the viability of new geographies
- Letting you implement personalization at scale in your outreach and campaigns
- Driving the success of your account-based strategy
The best predictive platform for your sales team isn’t the one with the biggest army of data scientists. It’s the one that can prove the highest value to your business.
The combination of intelligent predictive technology and the right use case for your team will result in a win for everyone.
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