Einstein, Watson, Sensei, Leonardo. What do these people have in common? What if I say they’re not people? They are AI suites of the major, world-known technology vendors. Though each of them has its unique properties and helps businesses make more informed decisions faster, today we will talk about Salesforce’s Einstein AI. Why? To date, it’s the most user-friendly AI suite with a broad potential for various practical uses.
Launched in 2016, it made Salesforce one of the smartest CRM platforms in the world (although there are some powerful Salesforce alternatives on the market), bringing the power of artificial intelligence to every Salesforce user. With each new update, Einstein accumulates more advanced features and gets easier for non-tech users. At the same time, it provides wider customization capabilities for those ready for all the programming fun.
One of the latest brilliant applications of Einstein AI is the visual identification of great white sharks present near popular beaches in Southern California. This way, the AI experts and oceanographers are trying to keep beaches safe, protect the populations of great white sharks, and help people learn how to share the waves with the wildlife.
However, like any sophisticated mechanism AI is still a source of confusion and controversy. According to most Salesforce services providers, that is the case with Einstein AI, too. Many users don’t quite understand how Salesforce Einstein works and how to leverage it. So, when businesses purchase Salesforce, Einstein feels like the black box with no key to it.
Let’s look at the bigger picture of Salesforce AI and explore how to use it with a view to increasing ROI and outrunning your competitors.
What is Salesforce Einstein?
Salesforce Einstein is a set of advanced AI capabilities that help users get smarter insights from their data in order to deliver personalized customer experience, get proactive recommendations for the next best actions, and automate routine tasks.
Einstein analyzes your historical data against set parameters and creates data models that are further trained on huge data sets. When fresh data comes in, Einstein double-checks whether the previously created operational models are still accurate, and updates them in case they’re outdated. This way, Einstein-based predictions and recommendations always stay up-to-date.
Einstein differs from other examples of enterprise AI. It’s not a standalone solution—it’s integrated into each Salesforce product. This way, it can’t compete with IBM’s Watson, for example. Einstein works within the context of the product you use to provide insights on the basis of the data you feed into this particular product.
It also means you don’t need to dive deep into the specifics of artificial intelligence and hire a team of data scientists but can start using AI on the go along with the whole Salesforce system.
Einstein AI as a platform and a product
Users often get confused about the nature of Einstein. It’s difficult to tell whether it’s essentially a platform (because it’s embedded into Salesforce solutions) or a set of products (because there are about 30 Einstein-based products, such as analytics, chatbots, object detection tools, etc.) Well, it’s actually both.
Einstein AI can be treated as a platform because it is at the core of each product. So, when customers buy a Salesforce solution, they get Einstein as a part of the system.
However, while using Salesforce, you will see a number of Einstein apps that serve as add-ons and need to be connected or even customized.
Such apps can be of three types:
1. Out-of-the-box apps: come as pre-built and can be added by users or Salesforce admins.
- Examples: lead and opportunity scoring, product recommendations, email auto-segmentation, etc.
2. Point-click solutions: require model creation and training, but can be configured by a Salesforce admin.
- Examples: analytics, service chatbots, custom predictions, etc.
3. Programmatic AI services: require extensive model creation and ML training using APIs to source unstructured data from integrated apps.
- Examples: object detection, intent analysis, image classification, etc.
Here’s a quick overview of the unique combination of Einstein’s advantages that differentiate it from alternative solutions:
- User-readiness: as Einstein is built into the Salesforce platform, it works seamlessly within your Salesforce processes.
- No need for data science expertise: it’s possible to use Einstein without preliminary training as it does the heavy lifting of data processing modeling.
- Custom-friendliness: in case there’s a need for advanced customization, Einstein provides services that can be customized by coding.
- Accurate data models: Einstein checks whether the created data models are adequate and updated at all times.
- Automation: Salesforce Einstein automates manual data entry as well as workflows based on predictive analysis.
How to get started with Einstein
Though Einstein is embedded into the platform and can be used alongside any Salesforce product, you still need to put a lot of thought into increasing its efficiency and preventing things like bias and false positives.
Where should you start? Hop on the Get Smart with Salesforce Einstein trail of the Trailhead training center and consider taking the following steps.
Check how ready your company is
Prior to starting with the software, answer a few questions to ensure Einstein’s fast adoption:
1. Does your company have the culture of analytics?
In order to come up with more precise predictions, Einstein needs constant learning, which means your teams will have to feed the platform with huge amounts of data. However, the culture of analytics means that your employees will do it not just because they have to, but because they value it. So, make sure your employees, from top-tier to front-line departments, regularly enter and update data, create and share reports, and make decisions driven by data rather than intuition. You can find out more in Salesforce's guide to predictive analytics.
2. Is your data clean?
Though Einstein does manage data processing, you need to make sure that your input data is of high quality. Otherwise, if you feed ‘garbage’ into the system, it will give you garbage back. To avoid this, you will need to clean your data prior to inputting it. The data should be complete and up-to-date as well as clean of duplicates, incorrections, and imprecisions.
3. Have you identified viable use cases?
When you start using Einstein, you are unlikely to expect just some occasional insights and predictions. You surely have some goals in mind. Thus, you will need to identify use cases, or examples of the tasks you plan to get done with Einstein, such as calculating the lead conversion propensity or transferring routine issues to chatbots, for example.
4. Are you ready for Einstein budget-wise?
Einstein AI is beneficial in the long run, but its adoption involves direct and indirect costs, such as licensing fees, data cleansing and transformation, user training, etc.
Start out of the box
In order to quickly embrace the new system’s capabilities and adopt it as soon as possible, it’s better to start with the default apps provided by Salesforce. They don’t require model training, as everything happens in the background.
Each Salesforce product has a number of such apps, which can be purchased apart from your Salesforce license. Let’s overview the most useful apps grouped by Salesforce Clouds:
Einstein in Sales Cloud
Einstein gives sales reps lots of opportunities to be more productive and exceed their quotas by providing actionable forecasts and automating repetitive tasks like data entry.
- Einstein Opportunity and Lead Scoring
Einstein can predict the likelihood of closing an opportunity and assign scores to leads (based on the history of lead scoring and conversion) so that sales reps can prioritize their leads more efficiently.
- Einstein Email Insights
Based on customer data, Einstein recommends the most relevant content and optimal send times.
- Einstein Account and Opportunity Insights
Einstein not only uses historical data to drive insights but also looks for structural changes within your accounts and opportunities, such as possible mergers, expansions, or managing board’s changes.
Einstein in Marketing Cloud
AI in marketing is directed at knowing more about customers in order to personalize their experience and boost their engagement across different channels.
- Engagement Scoring
Einstein AI analyzes each customer’s data and creates data models to predict how likely customers are to engage with your content, such as to open an email, click on a link, or look through an offer.
- Einstein Segmentation
The tool builds personas based on data from all the sources connected to Salesforce, which can be used to analyze potential audiences by channel and personalize customer experience.
- Social Studio and Einstein Vision
Leveraging social listening tools, Einstein sorts social media posts that mention your brand according to specified filters, for example, negative sentiment.
Salesforce’s asset, Einstein Vision, is able to recognize images. For example, it can detect which brands are present in the image.
Einstein in Service Cloud
AI in service improves customer experience by dramatically reducing hold time with the help of chatbots. It makes customer agents’ life much easier by automating their mundane tasks, such as case classification and routing.
- Einstein Agent
With this tool, Einstein helps service agents to keep customer satisfaction levels high, for example, by analyzing agents’ availability and wait times, in order to provide more timely responses. With the Next Best Action option, the tool also suggests in-context replies to customers’ common questions as well as recommends the agent to route the case to. It speeds up the overall case resolution time, especially when a service agent is on the phone.
Einstein Agent also saves service agents lots of time by automatically classifying cases and providing all the necessary information on customers prior to routing cases to them.
- Einstein Chatbots
Though Einstein bots can’t start operating automatically—they need to be built and fed with data—with each new Salesforce release, the process gets easier, and the chatbots more powerful. Once launched, Einstein chatbots can relieve your service agents by handling routine requests and learning to tackle more complicated issues.
Einstein in Commerce Cloud
AI in commerce learns about customers across multiple channels to deliver highly personalized and unified experiences across all touchpoints. It takes over your team’s manual tasks, such as merchandizing, updating customer segments, or creating new product groups.
- Einstein Recommendations
The tool uses the data on customers, even new and unlogged ones, to tailor recommendations, personalize and automate merchandizing on each page.
- Einstein Predictive Sort
The tool tailors search and category pages on the basis of shoppers’ actions both on web and mobile sites or apps, in order to help people find exactly what they search for.
- Einstein Search Dictionaries
The tool registers site searches, both via a search box and storefront browsing, to spot trending queries that are not in your dictionaries yet. Algorithms can further recommend related searches to shoppers.
Einstein AI challenges
Salesforce Einstein is a sophisticated algorithm that needs continuous learning. To make this learning efficient, you need to ensure two conditions. First, you need to feed it with huge amounts of data and, second, give its learning the right direction. These two prerequisites are usually the major challenges that may hamper successful Salesforce AI adoption. Let’s look closer at these challenges.
Salesforce Einstein needs considerable amounts of data for it to be able to look for patterns and trends, make accurate recommendations, and eliminate guesswork. When your company doesn’t hit the required data minimum, even when using out-of-the-box apps, Einstein probably won’t be of any use to you.
How would you know you have the right amount of data? Salesforce has a few tools that can evaluate your company’s readiness to use this or that Salesforce functionality.
Salesforce Optimizer Report
Creates a personalized report with recommendations on how to improve your implementation. The report can be run prior to installing new apps.
Lightning Experience Readiness Check
Analyzes your company’s readiness for the transition to the Salesforce Lightning Experience edition.
Einstein Readiness Assessor
Evaluates your company and creates a report with the recommendations on what to fix in order to use Einstein efficiently. It’s available only in Sales Cloud.
There’s also the challenge of data quality. Though Salesforce Einstein cleans, prepares, and analyzes data once it’s imported, you still need to import clean data. Einstein is smart, but it’s not a magician. It can’t transform rubbish into accurate insights. That’s why, prior to feeding any data into Salesforce, you need to make sure it’s free from duplicates, gaps, and errors.
Use case challenge
Einstein AI is not a one-size-fits-all solution. Each Salesforce product will deliver specific insights. Consequently, prior to acquiring a Salesforce solution to use it along with Einstein AI, companies need to define viable use cases and collect data to support them.
What is a use case? An AI use case is a specific way AI can be used to benefit your company. Use cases are important for understanding what Salesforce product you need to purchase. Identifying and prioritizing a use case is one of the major challenges for users.
Salesforce recognizes this adoption barrier and suggests tackling it with Salesforce Einstein’s Guide to AI Use Cases. It’s an interactive website with 50+ examples of use cases, which can help jump-start your AI efforts. It’s kind of a step-by-step questionnaire that leads you to picking a relevant AI use case.
The guide also gives recommendations on Einstein products and features you should use to support your chosen use case. The best part is that the guide is ever-evolving—new use cases are constantly added.
Exciting recent and upcoming features
Salesforce is evolving non-stop. With major updates three times a year, Salesforce adds new advanced features and improves its existing stack of tools.
One of the most exciting recent features is Einstein Voice that is still in beta. It allows you to talk to your Einstein Voice Assistant to get daily updates, take notes and relate them to associated records, or open your Salesforce dashboards. You can also create custom Einstein Voice Bots.
Einstein Voice kills the main pain of mobile employees and field sales reps—the need to pause to enter data. In the future, Einstein Voice is to be deployed to multiple devices, including mobile phones and smart speakers.
It’s also worth mentioning the recent innovations in email personalization with Einstein. The features such as Einstein Content Selection, Einstein Copy Insights, and Einstein Messaging Insights help marketers optimize their choice of images and email copy as well as content placement and send times, in order to improve customer engagement across email campaigns and to free marketers from time-consuming tasks.
For the time being, there’s a long list of the Einstein features that will be available with Salesforce Winter’20 Release, such as:
- Quarterly Forecasting that will make forecasting even more precise and accurate.
- Salesforce Einstein Search that will replace Salesforce Global Search. It’s basically an enhanced internal search that will deliver personalized search results to end users with the possibility to use conversational search and receive search recommendations. The enhanced search function will be able to deliver more relevant results based on the user’s geographical location or by picking up specific Salesforce jargon. The AI will also be self-training on the basis of salespeople’s territories or verticals.
- Code-free Predictions on Records that will let users create prediction scores for selected fields automatically without coding.
Ready to get smarter with Einstein AI?
I hope this guide has filled all the gaps you had about what is Einstein AI.
While this is a user-friendly module that will evolve as you use your Salesforce products, you’ll need to tackle some initial adoption challenges, such as having enough data and identifying your AI use cases correctly. Once you’re ready with this, you’ll be able to welcome the opportunities to automate time-consuming tasks and eliminate guesswork in strategic decision-making of any team, from marketing to service.