1. Pick 3 burning issues to fix / improve or new initiatives to generate value. What is important is having a clear picture of the full vision and selecting a technology stack which will support adding functionality as required.
2. Identify metrics to measure the benefits for e.g. increase conversion of website visitors by x percent, decrease admin time to process sales by x days, etc. Start with a baseline measure even if it is an estimate. Build dashboards and reports which can readily show the improvement in the metrics.
3. Identify one person or team to drive and deliver the implementation. Has the mandate to make decisions or can easily get to decision makers.
When it comes to managing the overall change:
4. Ensure that the leadership team is fully bought-in and understands the impact of the new system on the ways of working (the change will not only be a system change but will require changes to processes and how people work)
5. Make the case for change and ensure that it is commonly understood (the why, what, how, etc) across the organization. Most people would agree that emails and spreadsheets are not the most efficient way to work but changing well-established habits and getting someone to log onto a new system and do a task differently requires change effort. Must have a clear answer to the question ‘What’s in it for me?’
6. Don’t underestimate the need for training and communication. These are just as important as the technology and directly lead to better adoption of the system.
Zoho Creator is a useful and versatile platform. One of the more common requirements, especially when creating a new app, is to import data. While it has a decent import tool there is a limitation worth noting.
Using Numbers on a Mac I was unable to select a Numbers date format which was compatible with the Zoho form date format. The format is dd-MMM-yyyy. This cannot be changed – or I couldn’t easily find where to change this. Numbers does not support this format. As a result I had to use Google sheets.
When measuring adoption it’s useful to see the number of logged in users for a given period as a percentage of the total user base. Since Salesforce reports can only report on what data is visible (as is to be expected) it’s not possible to compare logged in users to total users. There is a way around this by using a joined report.
Create a joined report using User as the primary object. This will create a Users block.
Drag a field from the Users object over to an empty space in the preview area. This will create a second Users block. It’s a good idea to use a field which is required for reporting purposes since the report will be grouped at this level.
Filter the second block on logged-in users for the desired time frame.
Create a cross-block formula field using the Record Count summary fields from each block to divide the number of logged in users from block 2 by the total number of users from block 1. This field will typically be called Logged In Percentage or something similar. Change the formula to field type to percentage.
One of the more painful administrative tasks faced by Sales reps and account managers is keeping contact lists up to date. Sure, many platforms offer mail and calendar integration but there is still some work to be done, i.e. the contact has to be loaded in the contact application, such as Outlook, before it can be added as a Contact in the CRM platform.
Recently, Apple introduced a feature whereby, an incoming caller number is tagged with a potential contact name based on iOS matching the incoming number with a number in the email signature of an exchange with the contact.
Salesforce Einstein provides a similar benefit which is called Automated Contacts.
This is a simple but beneficial time-saving device which highlights the benefits of embedded AI technologies in a CRM platform
CRM vendors are increasing their investments in AI technology and making it available in their CRM platforms to support decision making and to trigger actions.
AI encompasses machine learning and deep learning as well data science methods. It is closely linked to the field of data analytics.
AI is about thinking machines. Data is ingested and patterns are detected using statistical models. These patterns can be used to predict behaviour and a host of other outcomes.
These benefits include predictive scoring (lead scoring), forecasting (predict future revenue), recommendations (for cross sell purposes) and early warning systems (for retention). In other words, AI will eclipse what generic algorithms offer, will enhance salespersons’ intuition and will intelligently monitor customer activities.
Other benefits include chatbots, virtual customer assistants, intelligent social media monitoring and automatically logging customer interactions.
Having made the investments the software marketing machines are kicking into high gear so there is an element of hype. However, the benefits for successful early adopters could be significant so be on the look-out for opportunities to experiment with the ever-expanding and quickly maturing AI toolset.
Finally, as always, as with all things CRM the quality of the data will make the difference between a mediocre outcome and a great outcome.
CRM (Artificial Intelligence) AI vendors and tools include:
Conversica – automated sales assistants
Introhive – data automation and sales acceleration
Salesforce.com Einstein – AI built into Cloud products
DigitalGenius – automate customer service
Microsoft Cortana – marketing, sales and service analytics solutions
Resistance to CRM adoption can be overcome by satisfactorily answering a number of key questions.
Answering the ‘What’s in it for me?’ question. This implies demonstrating the value and utility of the CRM application. In other words it’s about ensuring that the CRM implementation addresses recognised business needs. Answering this question is fundamental to successful adoption as it facilitates the buy-in and embedment process.
Have users at all levels been involved in the initial and ongoing design? This is important to build a sense of ownership, overcome us versus them thinking and incorporating the ‘what’ and ‘how’ aspects of the application. Many times a solution design which does not involve users will meet the requirements but not in the way that the impacted users expect the requirement to be addressed which means that their experience will be less than optimal and usage will suffer. This must also, importantly, address expectations of how data will be captured, processes will be made easier and data from other systems will be surfaced in the CRM application.
Has the change been communicated effectively and is there evidence that it has been understood in the context of the impact on users current way of doing things? Often overlooked, communicating the rationale for and positive impact of the CRM application will reduce noise, aid adoption and align expectations.
Is there adequate training material across a variety of training modalities? Good training and related training material are essential to overcome the initial friction users will experience when starting to use the CRM application. It’s frustrating when starting to use an application for the first time and not having the right training guides readily available. It’s also beneficial to make liberal use of inline help and context aware help tools.
How will data quality be managed? The single biggest inherent risk to the success of a CRM application is poor data quality. Poor data quality seriously undermines the credibility of the application and will retard adoption and usage. Data quality should address accuracy, completeness, relevancy and currency of data.
How will ongoing enhancements and support be managed? Effective CRM applications evolve with business and user needs. Enhancements ensure that the application remains relevant to the business, smooths over user frustrations and aids with maintaining good data quality. A good support model addresses support resource constraints, user issues, provides useful input to training content and potentially reduces commonly occurring issues by implementing enhancements to address these issues.
Big data, in the context of CRM, relates to large volumes of data used for (mostly predictive) analytics.
Data can be collected from various sources including customer channels, transactions and other customer activities such as product usage.
By applying analytics to these large volumes of data customer patterns, associations and trends can be identified. This can then be used to predict behaviours and outcomes.
Benefits can include better decision making, predictive modeling, and benchmarking.
This means that, for example, Marketing, Sales or Service reps can be equipped with insights to identify hot leads, close sales faster, predict when service issues can blow up.