Salesforce Einstein is a wonderful tool, but it may be intimidating for beginners to start with. Here in this article, we will explore some common problems beginners may face on Einstein and how to diagnose and fix these. When you have chosen to use Einstein, there are some crucial factors to look at. You need to look at it carefully to ensure you get the best.
If you donot address these fundamental things early in the process of establishing Einstein, it may cause more issues down the road. So, avoid problems in the beginning.
Business issues on Einstein
Salesforce Einstein analytics starts with setting up your goals. You need to know what you have to achieve if you want Einstein to deliver the same. You may want to increase your business's revenue, improve your profitability, boost the lead conversion, or reduce the customer churn rate, etc. When your Einstein implementation fails, the project team may not agree with the key stakeholders on the set goal metrics and scope. So, the stakeholders should agree on the common goals from the very beginning. Even if you have already implemented Einstein, you need to go back and take a look. Here are some basic tips to follow.
- At the first point, you should limit the scope of your Einstein goals to a single channel or team. Doing this will make it easier for you to measure your success. For example, other than setting generic goalslike ‘reducing attrition to 5 %’, you need to set a specific goal as ‘reduce customer attrition rate for the commercial segment to 5 % versus the current 10% rate.
- Next, you need to measure the results towards your goals set within 6 months of it. In this time window, you will influence the short-term goals like setting up the campaign results, the productivity of sales reps, or conversion of leaves, etc. If you try to set a longer timeline, there may be other variables affecting your goal results. This may raise more risk, and the measurements may also get affected.
It is also very important for executive technical teams and businesses to agree to the reasons as to why Einstein is not successful. You should document all these lessons learned and align them to the new goals.
Unclean data - Einstein's success may directly be affected by the lack of clarity and reliability of your data. These types of issues often start with your basic data, which is extracted from the source systems. This problem is passed on to Einstein. As Flosum explains it with an example, data is like flowing water from a source. Imagine your region is supplied with water from a lake. Which is dirty, and the authority pipe it directly to your home without investing in any water treatment facilities to deliver clean and safe water. This is the same in the case of your data sources; too, as if you do not invest in reliable data filtering tools, then your entire Einstein operations may be misdirected.
Data interactions - If the dashboards and reports become unreliable and Einstein data merge into it, data fields may be coming intermittently. You need to do the source transformations and migration processes. You should also confirm that there are no broken links.
Inaccuracies of analytical models
Predictive analytics models may be inconclusive or inaccurate due to many reasons. There may be underlying customer segmentation changes, becoming different from the models that are built for, and data science teams lack adequate skills to model accuracy.
- Duplicate or redundant records - To resolve this thing, you need to install some deduplication tools and build rigorous methods for customizing it based on your needs.
- Conflicting data- Before you load data to Einstein; you should centralize the data processing to resolve any mismatches or differences across the fields.
- Inaccurate or incomplete data- you need to create solid validation processes and append any additional or recent data by excluding inducible data.
- Word prediction accuracy for different models- data preparation activities like transformation, cleansing, and formatting should be fine-tuned based on your data model.
Another important challenge faced by Salesforce Einstein is the issues related to converting from your legacy systems. Sometimes, the handoffs between information technology and analytics teams may not be coordinated well. One team may add the tables of data without informing the other. This may affect the overall performance. Here, we will discuss some ways to identify adverse impacts on performance and how to fix those.
- Managing too many processes and flowsrequires creating a central Staging table to coordinate different processes versus Salesforce direct feeds. Try to streamline redundant floors if not completely removing them.
- Loading more jobs and slowing down the systems – More jobs may have more resources. You need to evaluate the volume, latency, and frequency of loading jobs. Try to prioritize the jobs and restructure to reduce the load.
- Data volume and granularity-Try to consider some external database structures like Heroku to accommodate its needs.
- Unexpected changes in various data fields and reports - Try to improve the documentation's effectiveness and try to check authoring rights, ownership, and permission specifications.
In summary, you need to go back to your Einstein implementation basics, review the vision, and plan to streamline it. One should diagnose the course of any discontent across the structure of data or user changes. By doing this effectively, you will easily identify the weak spots and work on them. You may also focus on the problematic areas to make the earliest possible corrections.
You may also restart a pilot process with limited scope and share your learning experience. If you find any success in this, document it and roll it out to gain more adoption. If the changes made by you are not truthful, continue to the next diagnosis and follow this pilot process again. Doing this genuinely will help to make a lot of difference in your Einstein experience.