Artificial Intelligence | News | Insights | AiThority
[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

Six Ways to Get the Most of Your Investment in Salesforce Einstein

Salesforce Einstein Analytics helps business users better understand and act on rapidly-changing information.

By the end of 2020, 30 percent of B2B companies will use AI to augment at least one of their primary sales processes. Most of them will turn to Salesforce Einstein, the market leader’s comprehensive AI for CRM.

It’s easy to get up-and-running on Einstein, but harder to leverage it properly and achieve Salesforce’s 478 percent five-year ROI. Here are three steps to take to get the most out of Salesforce Einstein.

Define Your Targeted Business Outcomes

Einstein Analytics (EA) templated apps make it easy to create an app with just a few clicks. But that means companies can quickly jump into an out-of-the-box setup without fully understanding their purpose for using EA in the first place.

Before diving into a templated app, establish what your company is trying to achieve with EA. Are you looking to …

  • Acquire customers more efficiently?
  • Grow revenue and profitability per customer?
  • Improve customer loyalty over time?

Each of these questions might require you to start with a different templated app. And remember, just because a templated app matches your industry doesn’t mean it will produce the outcomes you desire.

For example, a bank marketer looking to show how their campaigns impact their organization’s profitability might gravitate toward the Consumer Banking Starter Analytics Template. But that template is intended to help increase revenue per customer – and the process-specific Campaign Analytics Template could better serve their marketing goals.

Establish How You’ll Measure Results 

From there, take it one step further by quantifying the information you’re seeking. If you’re looking to acquire customers more efficiently, define exactly what an efficient customer acquisition looks like. Is it when the sales cycle, from the first contact to close, is less than six months? When a deal is closed with five meetings or fewer?

Not only will defining your business outcomes (and identifying the data points needed to measure them) help you select the proper templated app, it will make it easier to ensure your data is ready for EA later on in the process.

By proactively identifying and quantifying desired outcomes, you can ensure the selected template actually delivers on your expectations.

Take Stock of Relevant Data Points

After you’ve determined your business outcomes associated with EA, it’s time to make sure you have the data available to achieve them.

That’s right – the first step in making sure your data is ready for EA is making sure it actually exists.

While this might seem simplistic, many companies assume they have all the data they need simply because their dataset is 50,000 rows long.

For example, if you’re looking to uncover the characteristics of a successful deal, but aren’t tracking every corresponding metric (customer contact, length of call, meeting date, etc.), then it’s impossible for Einstein to tie those pieces together and deliver the insights you need.

Ensure Data is Clean and Ready for Salesforce Einstein

Related Posts
1 of 1,178

Once you know you have the right data, you need to make sure it’s clean. Clean data is critical to reliable analytics.

Inaccurate, incomplete or unreliable data will always produce inaccurate, incomplete or unreliable outcomes. As the common computing adage goes: garbage in, garbage out.

To ensure your data is clean:

  • Centralize processing to resolve differences across data sources.
  • Create validation procedures to ensure varied inputs will lead to uniform outputs.
  • Confirm linkages across sources, transformations, and migration processes.
  • Modify source datasets with transformations to add value for analytics (standardization).

Standardization is perhaps the most important aspect of readying data for EA. Unfortunately, it’s also the most overlooked. With proper standardization, you can ensure your data model will source the correct data, transform it correctly, and load outcomes into the proper dataset.

Establish the Right Location for Your Data Model

The data model is a framework by which Einstein processes datasets into desired outcomes. Even with great planning and clean data, the wrong model won’t give the anticipated outcomes.

You can build data models for Einstein directly in Salesforce, in Salesforce-integrated platforms like Heroku, or outside of Salesforce altogether. Data from external sources requires a lengthier standardization process, but for some companies it’s the only option to pull data into one place.

There isn’t one singular way to build a data model that fits your team’s Einstein needs. But it’s important to give your model a solid foundation by housing it within the right data platform for your organization and its targeted outcomes.

Build a Data Model That Supports Your Outcomes

To ensure your data model is set up to support reporting, predictions and delivery of Einstein recommendations, it needs to:

  • Accommodate data from different sources.
  • Align with the end requirements of different business units.
  • Allow for storage of transformed data.
  • Include frequency, volume and field details.

For example, say a global account manager wants to merge customer data from offices on two different continents.

One location uses Salesforce data, but the other uses a different CRM. Their data model would need to accommodate the two types of source data, which means including the same fields, volume, and security settings on each platform. This ensures the model delivers outcomes in a way that makes sense for both managers to use.

By feeding clean data through a model that follows a logical framework to address your expected outcomes, you’ve ensured you’ll receive accurate, helpful reporting, predictions and recommendations from Einstein.

Leverage Findings to Realize Target ROI

Salesforce Einstein Analytics helps business users better understand and act on rapidly-changing information.

But it only delivers relevant information if it’s set up for success with target business outcomes, clean data, and a fitting data model.

By following these three steps, users can ensure they’re getting the outcomes they need – so they can leverage them and achieve their full Salesforce ROI.

Leave A Reply

Your email address will not be published.