Simplify Decision-making In Departments With Self-service Business Intelligence Software

Simplify Decision-making In Departments With Self-service Business Intelligence Software

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Simplify Decision-making In Departments With Self-service Business Intelligence Software – Despite popular belief to the contrary, enhancing customer service (CS) can lead to better customer experiences and more efficient business models. A measure, if done correctly, will:

However, to get the above benefits – be prepared for a long and complicated work. The first important step towards success is defining the right objectives and key results (OKRs).

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Simplify Decision-making In Departments With Self-service Business Intelligence Software

While the overall goal is to understand large-scale customer support, it can be difficult to determine the right measures to know whether you’re winning or losing at any point in the journey. Identifying the right strategic key results (KRs) that will drive key KPIs is more difficult.

Understanding the Concept of Self-service BI

Definition and Overview

Self-service BI refers to software that allows non-technical users to access and analyze business data. It democratizes data analytics, making it accessible to all levels of an organization.

Importance in Today’s Business World

In an era where data is king, self-service BI tools have become indispensable for organizations. They provide a competitive edge by enabling quick and informed decisions.

Key Features of Self-service BI Software

User-Friendly Interface

One of the hallmarks of self-service BI software is its user-friendly interface. This feature enables users from various departments, regardless of their technical expertise, to easily navigate and use the tool.

Data Visualization Tools

Data visualization is a critical component of self-service BI. These tools transform complex data into understandable graphs and charts, aiding in clearer decision-making.

Advanced Analytics

Advanced analytics capabilities allow for deeper data exploration, helping departments uncover hidden insights and trends.

Simplifying Decision-Making in Departments

Case Studies and Examples

Through real-world case studies, we see how different departments have leveraged self-service BI tools to streamline their decision-making processes.

Benefits for Different Departments

Each department, from marketing to finance, gains unique advantages from self-service BI, such as increased efficiency and accuracy in decisions

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Goals based solely on the number of contacts will be short-lived, especially if your business intelligence software is constantly expanding your customer base. Rather than inspiring your team, your goals may cease to have any meaning and may even demotivate them. Relying too much on the total number of contacts can lead to understaffed customer support teams and long customer service firefighting practices.

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The metrics and goals set in this relationship will provide valuable information on the effectiveness (in terms of customer experience) of your entire business ecosystem. The lower the engagement, the more seamless your customer experience.

As for the nominal number of CS contacts, add it to the chart as an additional dimension – and get a combination that provides a more holistic perspective.

Teams that only report based on the blue line (#Contacts) and see such an increase in searches can falsely conclude that something is broken… “Contacts / Users” (red line) is enough to see that your system is really working well and even more effective. In this example, the only reason for the increase in nominal contact is because of sudden growth in your customer base.

Just by the number of contacts – you never know if it’s good or bad. You need a relationship to see the full picture.

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First, choose a definition that describes your customer base—this could be monthly active users (MAU), your entire (or active) email list, paying customers, or some other number that makes sense for your specific business environment.

This number should be tracked consistently over a long period of time, and it would be nice if you could retrospectively calculate the ratio over previous months and years.

Ideally, your ratio will be close to 2 at first, and over time you can see the numbers go down to 1 and below – symbolic success of your team.

Say you currently receive 10,000 tickets per week and have an email list of 5 million people. Multiply by 1000 – your current ratio is 2.

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If you take the above metrics and the expected user growth (based on historical data and strategic directions) – now you can define realistic goals in different ways.

Say you aim to be 30% more effective in 1 year. You cannot target the future by reducing the number of current contacts by this percentage.

If this seems like an ambitious but reasonable goal, add a 2.0 to 1.4 wide gold line to your graph – this will consistently show the direction and distance of a 1-year marathon.

Depending on your client’s progress, you may see the number of contacts increase while making the overall business more successful.

Heuristics: Definition, Examples, And How They Work

In order to improve profit margins, you may decide to increase your CS budget more slowly than business growth. To keep this example simple – say you decide to keep your CS costs constant.

If your current CS budget allows your team to properly cover 10,000,000 tickets per week, and you expect your user base to grow from 5 million to 7 million in a year, you can achieve a “Contacts/Users” ratio. time:

1.43 would be the target to achieve in this hypothetical scenario. Otherwise, you will face the dangerous consequences of crashing CS performance.

Such a mechanism provides a tool to predict future HR needs for customer service each week.

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If you have a reliable “Contacts/Users” target line (backed by a solid plan) and an expected weekly growth rate of your customer base – you can reverse the formula to calculate the expected #Contacts growth and then use this metric. Creating an expected growth curve for your CS HR needs every week during the planned period.

First, any effort to improve customer service involves the entire company that collaborates with that goal. Maximizing and improving the customer experience will not be successful if you do not give customer success (/support) the necessary authority within your organizational structure to influence customer experience decisions.

Second, support teams must gradually shift to customer success because the role of support is not primarily to answer tickets, but to do everything in their power to prevent and resolve the causes of customer queries.

Note that the pain point in this framework isn’t just related to bugs and objectively broken experiences. While a significant number of customers ask about pricing, this is a pain point that needs to be addressed. Literally any recurring customer question.

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For this key result, base the measure of success on the percentage of pain points that need to be resolved (eg, a reduction from 90% to 60% by the end of the quarter). Visualize progress each week based on how many pain points you have (a) solved, (b) ongoing, (c) to start with; because these 3 are important situations that describe your situation.

Why are we focusing on the number of remaining pain points instead of focusing on resolutions achieved each week?

Seeing how many experience mistakes your customers face every day will constantly bug you and your relevant stakeholders. A pie chart of remaining issues will be more motivating than seeing a few pain points resolved in recent weeks.

It would also be a clear signal to any executive who decides to cut overly ambitious CS budgets and set scalability goals without allocating the necessary resources to improve performance.

What Is Self Service Business Intelligence (self Service Bi)?

Ultimately, such a critical outcome will affect not only how good you are at solving pain points, but also how successful you are at preventing problems (factor 1). Don’t forget that any failure in customer advocacy and QA will result in regression of the overall KR #1 – so this setting provides a more holistic view of this important outcome.

This bottom line is how effectively you manage your CS channels and customer traffic. KR#2 should be based on measuring the percentage (%) of customer support traffic that comes through enhanced (self-service) CS channels, with the remaining percentage coming via direct email/phone/chat.

CS teams often strive to delight their customers by offering as many different CS channels as possible. Such an approach is extremely dangerous and threatens expansion efforts.

A large number of channels leads to a complex infrastructure (FAQs, search engines, response bots, chats, etc.) with many channels not working or having no growth measures – so it should be clear:

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For crystal clarity and tight control over CS traffic with all CS channel communication situations (TECH, marketing and other teams often communicate on CS channels as they wish).

Some companies with complex ecosystems may identify hundreds of such instances. For example, setting your CS direct email address as a “Reply” to platform-related automated emails (eg, checkout, login) will already result in multiple index entries.

Indexing all CS-related communications will provide a clear framework for implementation to drive KR#2.

Measuring both is ideal, but the 2nd metric can often be difficult to obtain (and thus must rely on approximations) – %SS Contacts is the best candidate for the ScaleCS model.

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SS Contacts expanded channels: help centre; contact form/chat with instant answer offers; chatbot, response bot, conversational bot chat or call engine; direct contact form/email/chat only available after self-service.

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