As an industry that capitalizes on the transfer and exchange of data, the telecom sector has a wealth of data on their hands they can use to stay ahead of the competition — network performance, product usage, customer information, billing details, and more.

This constant influx of data presents a lot of opportunities for telcos, but only if organizations adopt strategies that aim to make this data accessible and useful.

After all, with customer demand rising and technology ever-changing, there is a lot of competition in the telecom space. Only those who can extract value from data will be able to keep up.

Turning raw data into business intelligence comes with a lot of benefits for telco enterprises. Let’s take a look at some of these advantages.

1. Getting the information you need

Whether you’re a NOC engineer monitoring the performance and health of a network or a C-level executive deciding which project to fund, a business intelligence dashboard will help make a data-driven decision about potential network threats or inspire confidence in the new business direction.

Let’s take a call center manager as an example. The manager needs to determine the number of customer service reps that should be available during a given time. A simple descriptive analytics of the number of reported incidents received throughout the day would be sufficient for the manager to spot patterns in customer service demand and thus make the right staffing decisions.

Should they need to get into more detail, such as type of incidents reported to ensure availability of the right skills, the manager can drill down or drill through the report for further insights.

The same applies for when you’re using more advanced analytics methods. Perhaps the manager is employing a predictive analytics model that forecasts the number of calls that might come in. Real-time, interactive dashboards that visualizes these predictions would allow the manager to identify long-term trends and even relationships between various data dimensions.

2. Better visibility of data — from networks to customers

Not only can business intelligence make information more accessible throughout the organization, but it also breaks down silos and provides a consolidated, more comprehensive view of data.

Telco organizations provide a wide range of services and functions and thus may utilize different systems to manage and maintain operations—from the infrastructure to the business side of things. Data may be generated from various systems, but data management and dashboarding allow you to bring information all into one place.

One of the biggest advantages of integrating and visualizing company-wide data is getting a better view of the customer to tactfully boost customer experience. When a customer calls in to submit a complaint, the representative on the line should be able to effortlessly pull up required information such as the customer’s billing records, service subscriptions, and even the company’s server status. Everything the representative needs would be available within a single dashboard. 

3. Insights and alerts in near real-time

The telecommunications landscape today requires an always-on mindset. Customers demand service reliability and availability, and organizations need to consistently provide these if they want to survive. In order to keep up with demand, leverage actionable BI and see key metrics at any given moment. This way, stakeholders will know immediately if SLAs have been breached, KPIs aren’t met, or if there is a service interruption.  

For example, customer complaints are inevitable, but they can also be managed proactively. With near real-time insights into network health, you can get a head start on issues as they occur. If a forecasting model is built into your telecom KPI dashboard, you can even anticipate these challenges. This will greatly improve customer satisfaction and experience. 

Real-time alerts can also enable predictive maintenance. Through a maintenance data dashboard, operators can monitor the entire system and get visibility into potential issues and downtimes. They can use this information to determine when to repair, or even upgrade, telecom infrastructure. 

4. Discovering valuable opportunities

While dashboards are great for accessing information and staying on top of issues, they also allow users to discover new business scenarios and valuable opportunities – whether it’s an idea for a new product or a gap in the market.  

For example, analytics can provide information about the adoption of existing products and services and which have the most potential for a rework. You can also find ways to reduce costs associated with service fulfillment by analyzing information regarding installation, upgrades, and repairs. Or you can address customer service problems by looking into customer feedback and reviews.  

By extracting actionable insights from raw data, you can refine pricing, operational, marketing, customer, and other areas in your business.  

5. A boost to customer delight

With customer data and insights at their fingertips, telcos can go a long way in terms of addressing customer needs and expectations. By leveraging business intelligence, organizations can provide targeted experiences that make customers feel valued and heard.  

Data insights also allow organizations to focus on high-value customers. The opposite also applies – customers that are likely to churn. With insight into who these customers are, you can customize campaigns and promotional messages to appeal to these groups for maximum business outcomes. Send them emails that include their product preferences, upsell services that they might need, and target them with ads based on their shopping habits.    

More than just dashboards — business intelligence is key

These are just some of the many ways telecom companies can benefit from a business intelligence implementation. By leveraging analytics and reporting capabilities, the telco industry can act with speed and certainty to make data-driven decision, optimize processes, boost revenues, and improve the customer experience. 

The post 5 ways business intelligence helps telcos gain competitive advantage appeared first on Sryas.

Dashboards—they’re the face of your analysis, the interface of your business. They help you organize and visualize your data and most importantly customize how you present intelligence to different stakeholders. They reveal valuable insights at a glance, keep you informed, and can chart your next steps to success. They ultimately serve the purpose of keeping everyone in the organization on the same page.

But dashboards are also a great way to strengthen your brand. Whether you’re working with a BI or an AI dashboard, its design and functionality provide the prime opportunity for brand building and promoting consistency in how your company is perceived—internally and externally.

A strong brand offers more than just competitive advantage. It leaves a memorable impression on both your customers and users, and it teaches them what to expect. With effectively branded dashboards, you are able to bolster your company identity, promote ease of use, and even positively impact organizational culture.

The importance of visualization

Before we get into the nitty gritty of branding dashboards, let’s talk about visualization and its role in analytics first. When you think about reporting, the more traditional business intelligence probably comes to mind first. But reporting actually plays a huge role in advanced analytics as well: machine learning, deep learning, and artificial intelligence.

Dashboards often offer a consolidated view of different business metrics through a real-time KPI summary. They tell you what’s happening in terms of customer churn, employee productivity, maintenance activity, equipment usage—whatever it is your company wants to keep track of.  Interactive dashboards take this a step further, allowing you to drill down into micro level details of information.

However, this sea of information and insights available can be paralyzing. Businesses need a more sophisticated way to visualize all this data, a better way to take in this information. This is where AI analytics and the AI-powered dashboards come in. Not only do you get insights from different data sources in real time, but you also get automatic alerts, valuable forecasts, and recommendations and prescriptions. This way, you can identify trends in real time and get insights that will help you remedy problems ahead of time and bank on opportunities sooner.

No matter the foundation of your analysis—business intelligence or advanced analytics—dashboards are an integral aspect of the process, making it all the more important to optimize them for your organization.

Establishing your brand with dashboards

Here are some things to keep in mind:

1. Know exactly what your brand identity is.

For any brand building project, the first step is to get a clear idea of what you want to exude. That means familiarizing yourself with your company: what products and services it offers, what its overall objectives are, even what its culture is like. Essentially, learn how employees and clients currently perceive it.

From here, you can isolate the important aspects of your brand personality and tie it together to determine how you want to present your brand—and dashboards—visually.

2. Aim for uniformity and consistency.

The key to effective branding is consistency. Now this doesn’t just mean plastering your company logo over all your dashboards. It also means choosing a color palette and sticking to it, and using consistent fonts and graphic styling.

To kick things up a notch though, you should also implement consistent visual management. This means making dashboards easier to digest and lessening confusion by keeping certain visual aspects uniform. This is especially significant for reports that are viewed on a regular basis. If you keep certain sections in the same place and use consistent colors for health indicators (for example, gray for low and green for high), users will be able to find the information needed much faster. 

3. Know your target audience.

There are two things you need to consider when optimizing your dashboards for branding: design and functionality. This is one of the reasons why building dashboards are so challenging. More than just making the design consistent, you should also make sure they are functional in a way that makes sense to the end user: executives, business managers, or even other data scientists.

For example, if you’re building a prediction dashboard for a hospital administrator that forecasts when a patient is at risk of readmission, make sure to set up alerts that are easy to understand, preferably ones that show up as natural language.

Or if you’re building a sales summary dashboard for your CFO, make sure to build well-designed drill-down paths and simple navigation. With a tailored report, the executive can easily find the root cause and get actionable insights for critical decision making.

Dashboards are here to stay

Despite what some people think, the age of AI doesn’t mean the death of dashboards. In fact, the growth of analytics just means the advent of the modern dashboard: intelligent, user-friendly, and chock-full of information to help users meet business goals.

So, take advantage of its role as a vehicle of insights—use the data analytics dashboard to bolster your brand identity and provide a better user experience for your employees, customers, and clients.

The post How to build a dashboard that aligns with your brand appeared first on Sryas.

Visualization helps you put huge sums of data gathered from web analytics tools into a visual format that’s easier to understand. It helps you gain insights, find trends and patterns, show large amounts of data at scale, and identify items for further analysis. After all, spreadsheets and lists just can’t convey the meaning of data the way a chart can.

One way to use data visualization with web analytics is through heat maps that reveal insights about user behavior.

Types of Visualizations

Before we go into heat maps, it’s important to know that there are other types of visualizations, and that which one you choose typically depends on the type of data, your audience, and its purpose.

Types range from common examples like pie, bar, and Gantt charts to more advanced variations such as Sankey diagrams, sunburst diagrams, and of course, heat maps.

What is a heat map?

A heat map is a two-dimensional visualization of data where values are shown with specific colors. Typically, intense colors (i.e. red) are used to display values that need attention.

In the following heat map example, which shows where users are clicking on a webpage, each value has its own colour. Higher values are displayed in green while medium and lower values are displayed in shades of yellow and red, respectively.

How can I use heat maps to improve my websites?

Heat maps are often chosen for displaying web analytics information because they reveal insights into user behavior, including:

• Which links are clicked most often
• How users scroll through a page
• Reading patterns
• Whether users are looking at and/or clicking ads
• If the interface is being used as expected

As examples, these heat maps show exactly where users spend the most time on a page and which links are most popular. You can use this visual data to improve your websites in ways that makes your content more effective and increases click-through rates.

A heat map analysis offers easy-to-follow insights into your website performance, allowing you to confirm the organization and content of your webpages and, if needed, adjust the position of popular links and content, improve ad placements, and fix interface issues.

Visualization allows you to use the extensive information gathered through your web analytics platform to make better decisions about your websites.  

The post How to boost your Web Analytics with heat maps and Data Visualization appeared first on Sryas.

Part 2: Optimizing the customer journey through Call Center Data Analytics | Read Part 1

After a sale, it is critical to ensure that every touchpoint with the customer is handled skillfully. It is only possible to understand and improve a customer’s journey by tracking key customer metrics and run analytics.

Telecom organizations that know how to identify such gaps in the service provided using analytics—to further enhance customer satisfaction, reduce churn, increase revenue and greater employee satisfaction—will see the rewards.

Churn is a common problem among almost all organization, but what is making your customers leave? What reduced customer satisfaction? And what is it about the interaction that upset customers? And what can you do to ensure customers have a better experience? These are just some of the questions keeping decision-makers up all night.

It is an even a bigger nightmare when you only have disconnected analytics to look through and make the best inference that you can. However, being able to rely on someone or a partner to answer these questions can take away the guessing. Our analysts have answered very tough questions and delivered results time after time with Analance.

Real case. Have a look at the infographic generated for one of our clients tracking every customer touchpoint for product support. Being able to identify business problems at a glance allows your organization to handle challenges more proactively and with just a few changes, you can expect for a turnaround.

Maybe hiring agents with better communication skills or prioritizing training on product features are some of the quick changes you can make to impact delivery performance and customer experience, which will further reduce the likelihood of churn and/or negative product reviews.

Better visibility of the end-to-end customer journey helps! Having a holistic view on call centre analytics, for example, will help you easily identify customer pain points. Using business intelligence and predictive analytics, call centres can know why, when, and for what reason customers are calling. Further analyzing customer feedback provides an opportunity to better understand behavior and get customer insight on how to provide a better customer experience.

Now, having all this data delivered to you in through an interactive dashboard or in the form of an infographic can give you a kick-start on executing a new plan faster. It is much easier to understand and digest (across the organization) than having to see day-to-day metrics in silo.

The post Call Center Analytics: How to optimize the customer journey with customer touchpoints data appeared first on Sryas.

Part 1: Optimizing operations through Call Center Analytics

Customer service is essential to any organization, especially for product-based companies offering support to customers around the clock. When an organization offer such services, it becomes pertinent to keep the customers happy, and that is only possible if the call centre operation is fully optimized.

Now, the question is: what makes a customer happy? One important thing to remember is that both happy and unhappy customers are likely to talk or share their experiences on the service received through multiple social channels. In fact, even one negative comment left on a social network can be a daunting situation to deal with because you cannot erase it.

To prevent such an ordeal, it is best to take preventative measures. Making the customer experience a priority will not only boost customer satisfaction and revenue, but also build a smarter call centre.

Real case. Of course, no two organizations are alike. But paying close attention to the year-over-year fluctuations (like what time of the day most of your customers will call for servicing or knowing what type of cases you are likely to receive) can provide valuable business intelligence—insights that can be used to make more data-informed decisions about resource allocation and elimination of inefficiencies.

We present our own client case where our analyst, with the help of Analance, converted rows and columns of call centre data into stories that helped optimize and restructure call centre operations. Our call center analytics solution ensure that agents are prepared to handle an in-surge of customers and also ensure that skilled agents are available to provide resolution on the first call.

Insights also highlighted the types of calls that could be deflected. To reduce the total number of calls routed to support centres, call centres can proactively address possible customer concerns through other mediums like FAQs and automated message broadcasts.

Based on the insights, managers can also manage agent start times, end times, and break times while call centre performance capabilities are always met.

The post Call Center Analytics: Optimizing operations with customer touchpoints appeared first on Sryas.

Part 2: Moving forward with Advanced Analytics

In Part 1 of our blog, we explored how to get started with BI Analytics in your organization, including the primary business challenges and how to overcome them.

BI Analytics is just the start though—most organizations are moving to Advanced Analytics with features such as forecasting, predictive modelling, algorithms, machine learning, and artificial intelligence (AI). Advanced Analytics is all about foresight and getting accurate predictions of what is likely to happen to your business.

However, as you’ll see in Part 2 of this blog series, making the move to Advanced Analytics includes its own set of challenges that you need to be prepared for if you’re going to make the most out of it.

To implement Advanced Analytics, you need to ensure you have staff who can do the job and you’re going to have to take a deep look at your business strategy for analytics. What are the use cases, business benefits, and expected results should you move to Advanced Analytics?

You also need to ensure leadership is on board with the right funding and internal support. Keeping the analytics algorithms, systems, and insights away from prying eyes is also important. A culture of security protects against threats such as corporate espionage, privacy breaches, and hackers.

5 challenges for implementing Advanced Analytics

1. Finding the right staff skills

It’s in the name—transitioning to Advanced Analytics requires that you have data scientists and other staff with advanced skills and experience in algorithms, AI, and machine learning. Overcoming this challenge sometimes just involves designing and encouraging professional development programs for your existing BI analytics staff to update their skills for Advanced Analytics.

However, for many small and mid-sized companies, even having dedicated analytics staff is impossible. It’s often more effective to consider making use of an analytics services provider that can help you plan and implement a solution that best suits your business needs and goals.

They typically have a wealth of experience implementing and transition Advanced Analytics for businesses large and small.

2. Defining use cases for advanced analytics

Defining use cases for something abstract like Advanced Analytics can be very difficult for most organizations. You need to figure out who in your organization will benefit most form Advanced Analytics, how they will make use of it, and which types are most appropriate for your business objectives.

Some ways to do this include internal brainstorming/focus groups, sending out questionnaires (based on possible business outcomes), and getting an external consultation for fresh perspective on possible use cases.

3. Defining your business strategy for advanced analytics

Defining a business strategy involves knowing your analytics audience(s) and use cases, sourcing the right data, and selecting appropriate algorithms and methodologies to get the insights that best fit your business objectives.

Many organizations struggle with figuring out how to best leverage AI, forecasting, and predictive algorithms. To overcome this, you need to work with your internal teams to audit your pain points, prioritize use cases, identify high quality data, and develop business-relevant analytics and models.

You also need to discover how to integrate analytics in front line tools that have the highest impact, and are easy to understand by your teams.

4. Getting funding for the transition

Convincing management of the importance of Advanced Analytics can be difficult. You need to do this so that proper funding is allocated to this endeavor. Some ways you can accomplish this are through demonstrating real use cases, examples of insights your staff can expect, and what the return on investment will be from deploying Advanced Analytics.

The solution you use for Advanced Analytics can also help you get proper funding. For example, using a modular, off-the-shelf Analytics suite can mean that the only costs, besides perhaps staff, are the addition of new Advanced Analytics modules related to your business strategy. It’ll be much harder to get funding if you use custom in-house solution that can’t be scaled without significant development time and investment.

Basically, handing it off to a third-party who has successfully delivered advanced analytics solutions many times for organizations of your size and with a similar objective. If you are curious about what Analance offers, read about its product capabilities.

5. Safeguarding privacy and security

Considering the number of severe IT security and privacy breaches that have occurred in recent years, you have to meet the challenge of safeguarding data, the analytics solution, and insights against hackers and other malicious threat vectors. You need to ensure that your organization is promoting a culture of security and has an incident management process in place to deal with any breaches.

You also have to ensure that the data you collect and the insights you receive don’t cross certain lines that would offend your customers, identify them specifically, or leave them open to attack.

Cybersecurity also needs to be designed into your Advanced Analytics solution from the start, including for the overall solution, integration layers, models, and algorithms. It’s also strongly recommended that you have your solution audited by an impartial third-party who will identify any problems areas and recommendations.

Taken altogether, transitioning to Advanced Analytics can be quite challenging. The right skills, use cases, business strategy, and funding can make this process a lot easier. To keep your organization and end users safe, you also need to make sure you protect the security and privacy of your analytics solution, data, and insights.

Make a more seamless transition to Advanced Analytics

Taken altogether, transitioning to Advanced Analytics can be quite challenging. The right skills, use cases, business strategy, and funding can make this process a lot easier. To keep your organization and end users safe, you also need to make sure you protect the security and privacy of your analytics solution, data, and insights.

The post [Part 2] 5 challenges of implementing Advanced Analytics appeared first on Sryas.

Part 1: BI and Analytics adoption made easy

Companies across many industries have already adopted BI analytics to streamline day-to-day operations, customer support, accounting, sales, marketing and other business functions.

If your company or department hasn’t adopted BI and Analytics, you may find that this post can help you or your team move into the analytics space—starting with BI and Analytics and evolving to consider leading-edge Advanced Analytics solutions with AI and Predictive Analytics.

Getting started with business intelligence analytics tools can be a daunting task. Your organization or department may have a ton of data, but to date has not used them to generate insights.

Connecting your data and starting the analysis is the easy part, but there are few challenges to overcome, including refining your business strategy for analytics, addressing risk and governance issues, and finding the right in-house or outsourced skills to do the job. Then there is the uphill battle to convince leadership with a proof of concept that showcases the value of BI and Analytics for your business.

This is Part 1 of our two-part series blog that will drill into ways you can address these challenges. We’ll explore 5 practical steps that will help you adopt an Analytics solution that is best suited for your business. Part 2 will look at the specific challenges facing organizations transitioning from traditional BI Analytics to Advanced Analytics.

How to adopt BI Analytics in your business

1. Have the right business case to get leaders on your side

The first step is to convince leadership to adopt BI solutions with a business case, which identifies all the business users most likely to benefit from an Analytics solution.

Every department in an organization has untapped data. So, in collaboration with the potential users—find, document, and analyze departmental problems that can’t be solved using primitive analytics tools and decisions that cannot be made solely on intuition or inaccurate data.

After you’ve assembled a list of problems, prioritize them and figure out which ones, if targeted, will yield the highest return on investment (ROI). Think about whether you can get management support, the type of data-driven insights the users will get from having a BI and Analytics platform, and how solving different problems aligns with business specific goals.

Also, consider the data sources: Are they available? How are they structured? Do they contain high quality data? Does your data have duplicates? You need the right data to get accurate results from analytics.

By looking at all these areas, you can identify the most ideal business case for analytics and gain the necessary support and resource to move ahead with your goals.

2. Understand the impact of analytics on your organization

The next step is to look at potential analytics solutions by networking with your peers, reading online resources, comparing vendor offerings, and analyzing documentation. These activities help zero in on the best analytics solution for your business.

At the same time, you need to look internally at your business to make sure current and planned infrastructure can handle the analytics solution. You also need to make sure that you have enough quality data for better, more reliable analytics results.

After you’ve done all that, make sure to consider your operational needs: What are your future analytics solution performance goals? How will they be measured? Can you measure them using available tools? Who will support the platform?

3. Find the skills to implement analytics

Adopting an analytics platform requires more than just the right solution—you also need to make sure you have the right skills, experience, and resources to plan, deploy, and manage the platform.

You need to find out if you have in-house skills or if you can build the skills internally. If not, you need to have budgets in place to hire new personnel or ask your choice of vendor whether they can manage your analytics platform or have a third party manage on your behalf.

4. Audit the technical requirements 

To ensure your IT infrastructure can support your preferred BI and Analytics solution, you need to perform an audit of your internal infrastructure and the solution requirements. This can include auditing the following areas:

• Data availability and quality requirements
• Existing and required data governance policies
• Security and privacy requirements
• Physical and virtualized infrastructure capacity and requirements
• Cloud hosting options (private, hybrid, or public)
• Specialized components and integration layers
• Required queries and algorithms
• Presentation layer for end users, including reports and dashboards

After an audit is complete, you can procure any required infrastructure, make appropriate improvements to your governance and security policies, and then finally plan, design, and implement the solution.

5. Implement the solution

With the right skills, technology, and knowledge in place for your BI and Analytics solution, you can start designing the solution. This includes identifying use cases, mapping data flows, deciding on what data is important, and selecting the correct analytics and algorithms for your business.

After this is done, it’s time to set up development and quality assurance (QA) environments to configure and test the data warehousing, application layer, integration layer, presentation layer, end user tools, and other components. Users can then be trained on the systems and you can deploy the solution in a production environment.

With these 5 steps, you can move your business towards the adoption of a BI and Analytics solution that provides better insights into critical business functions while offering real return on investment. 

The post 5 steps to adopting Business Intelligence Analytics in your business appeared first on Sryas.