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A Handy Guide to Ensure Success with Your Salesforce Einstein Journey




A Handy Guide to Ensure Success with Your Salesforce Einstein Journey

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Any journey with Salesforce Einstein starts by setting your goals. Being a sales team manager or a business strategist, you may know exactly what you want to achieve. Once you are clear about the goals, you may further use Einstein to deliver those. For example, maybe your goal is to improve the profitability, enhance revenue, reduce the churn rate of customers, or boost lead conversion; Einstein may come to your help in all these scenarios.

However, when Einstein implementations tend to fail, we may see that the internal project teams will disagree with the key stakeholders on the project’s objectives, scope, goals, and metrics. So, all the stakeholders have to agree on the objective and goals from the first point and stick to those until the end. So even though you have implemented Einstein, it is essential to explore the basics again and takestock of your clear goal sets. Some tips to help you out with this process as below.

Set the goal clearer

As the very first thing, you may try to limit the project scope of your Einstein implementation to a unique team or a unique channel. This approach will further make things easier to measure your success rate. Say, for example, rather than setting a standard goal to ‘reduce customer attritions by about 5%’, one may have to set the goal clearer as like ‘reducing the customer attrition for commercial segment below 5% compared to the current 8%’.

Measure the resistance towards set goals within a certain time period

You may set 6 months in the time window where you will be able to influence your goals for the short-term as campaign’s results, overall productivity, and the rate conversions of leads, etc. On trying to set a longer timeline to achieve or review codes, other variables may also affect the results. It may also reduce the risk that all the measurements being inclusive or invalid.

It is essential for your business executives and technical teams to agree on the reasons why Einstein has not become successful. You need to discuss and document the lessons you have learned and also align them with further goals. Further, let us explore some of the key issues related to the Einstein management system.

Data-centric issues

1. Data being unclean – The success of Einstein analytics can be largely affected due to reliability being lacking and compromised the accuracy of the data. This problem may often start with the data being extracted from the source and loaded into Einstein. You can consider it like water flowing from a source to your house. If the lake from where the water is pulled is dirty, then if the line is directly connected to your house, there are no chances that you get clean water while turning the taps on. So, it is essential to invest in a treatment facility that should clean and deliver safe water to your home. It is the same principle that applies to the data sources too. You need to ensure the accuracy and reliability of your data before feeding it into Einstein for Analytics.

2. Data interactions – Dashboards and data-based reports will be unreliable after setting up the Einstein platform and mapping your data to these. Data fields will be coming up intermittently where you have to recheck the transformations from the sources for migration processes. Also, you need to verify that there is no linkage broken, which may further damage the interactions.

Salesforce facilitators like Flosum will help you to better manage these challenges and make the Einstein implementation smoother.

Analytical model related issues

Predictive analytics and AI models may be inaccurate or inconclusive due to various reasons as below:

– First, change in the underlying customer segment.
– Data is now different than when the actual models were first built.
– Team members handling data science projects may be lacking insufficient skills to build the model accurately.

Let us further explore some other common issues too related to the analytical models and data and solutions for each.

Duplication and redundancy – For this, you have to try out tools for deduplication and use more rigorous methods to customize them for your specific tasks.

Data Conflicting prior to loading it to Einstein – Centralize the process, which will help resolve the differences across various fields and sources.

Incomplete and inaccurate – Create validation processes accordingly. Append the recent and additional data from time to time and exclude unusable or outdated data.

Poor accuracy prediction for data models – Data preparations, cleansing, transformation, and formatting to be done to fine-tune your analytical models.

Structural challenges

There are also many challenges and shortcomings to be expected while converting from a legacy system to a fresh system. Sometimes, the handoff between the analytics and IT teams will not be well coordinated. For example, any of these teams may be trying to add the data or the tables without intimating the other team, which will affect the performance of the other. Here are some ways to spot the impact of structural issues on the overall performance and the possible solutions for those.

Many data flows and processes to manage – You have to try and build a staging table centrally to consolidate the processes versus direct feeds on Salesforce. Then, you may streamline and remove any redundant flows.

Loading the jobs which further slow the system down – Jobs may consume more resources. So, it is important to try and evaluate the overall volume with the frequency and latency of job loading. Then, try to prioritize and restructure the jobs accordingly to help reduce the overall load.

Data volume as well as an increase in granularity –You may also try out some external DBs as Heroku or external objects.

In between changes in reports, data feeds, and results – Improve the documentation and handoffs by checking the ownership, authoring rights, and needed user permissions.

There can also be user-related issues during the implementation of Einstein. As a solution for these, to start with, you have to revisit the basics of your initial plan and vision. You may also try to identify the cause of disconnects across data structure and changes in users. By genuinely doing this exercise, you will be able to identify and define the vulnerabilities. Next, you have to identify the troublesome areas and make necessary corrections. You should also openly share the learnings and, if you find success, documented the same and continue to roll out and gain more adoption. Finally, if you find that your changes are not fruitful, continue to diagnose and follow the pilot process again. This may not be a terrible thing, as you may imagine now, because you are just isolating one or two changes at a time to identify the problem and solve it. As a result of this approach, we will have a better chance to solving all the challenges and gain better traction on Salesforce usage.

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