5 examples of actionable data analytics
Actionable insights, or as some call them actionable analytics, are deep and meaningful findings that stem from analyzing data. In the case of businesses, they usually refer to the analysis of customer feedback for the purpose of taking further actions to improve either a product or a service.
Companies use data-driven, customer feedback insights, in order to drive their products to the next level and ensure customer satisfaction. Data analytics calls for an artificial intelligence performance of tracking and analyzing multitudes of customer feedback comments and conversations, in order to produce insights about their consumption behavior. Once these insights are gathered and discussed inside organizations, they can make choices about their next business and market moves.
Insights based on data are becoming commonplace, since companies have to deal with millions of customers simultaneously and make strategic as well as business decisions fast. A company not relying on analytics and on quality data analysis risks taking wrong business decisions.
Customers make purchasing decisions and then they discuss them on many different online platforms. They may praise a product or a service, write a review, share posts or simply complain about it.
For organizations to make informed decisions based on what their customers need and want, they must track such comments, analyze them constantly and then take actionable insights based upon such big data analysis.
Every actionable insight must be based not on raw data but on structured insights. That means to take every piece of comment from whatever platform, add it to all the other comments from customers, and form a collective voice regarding the product or service.
Since different customers make different opinions, such data can be categorized into groups in order to form patterns and evaluate what are the most mentioned responses. Then a company can prioritize and make informed decisions about where to focus next.
For every customer writing a post and voicing an opinion, there are also many spam and bots-related comments. A smart artificial intelligence platform with the right digital process definitions can result in key knowledge points for businesses to digest and act upon. Data collection and its analysis, based on filtering out unrelated pieces of information, helps organizations to form true customer feedback conclusions.
Then, an actionable insight or several data-driven informed decisions can be made and take every company towards improving its products and services as well as giving their customers more value. Then every organization can meet its KPIs, make sure its strategy works, and achieve growth.
Actionable decision-making processes, based on big data analytics, can also serve each business team within an organization. For example, insights based on customer feedback data for the marketing department can result in much more focused marketing campaigns. Actionable insights for the product team may include adding new and improved features based on what consumers discussed on niche forums.
As long as the decision-making processes are based on feedback data analytics, companies can feel that they have the right tools and metrics to handle their next operational moves. If they wish to identify what their consumers are demanding further and gain knowledge about additional customer behavior patterns, businesses must maintain the process of tracking and analyzing their digital consumer voice.
Finally, actionable insights and data analytics must always evaluate the company's market and competition. Actionable insights and business strategies, as taken by organizations, must always adjust themselves to what happens in the market.
It is possible to track and analyze data regarding what customers are saying about a company's competitors, therefore enabling a business to identify its key benefits, or lack of, in a number of areas. Analyzing the competition enables a company to make strategic insights and actionable decisions.
What are the benefits of making data-driven decisions?
There are several benefits to making actionable insights and data-driven decisions. When data analytics on the consumer voice takes center stage, companies have better chances of improving their KPIs and delivering better products and services to their customers.
Constantly keeping in touch with what your customers need and want
Millions of customers create huge volumes of big data feedback that require analytics. But customer feedback is an ongoing process, as consumers keep voicing their opinions about products and services all the time.
They do it, just to name a few examples, by writing social media posts, or they review products in special boards or in google forums. A customer may also send an email to the company's website or customer service. An organized collection of such feedback points and their analysis may keep the company on the pulse of what their customers require.
Gain a competitive advantage by following your competitors and figure what their customers are saying about them
Following an audience in order to identify what they think about a product relates not only to the company but also to its market rivals. A customer may have an insight into a competitor's product, and once tracking of such data takes place, businesses can gather a clearer picture of where they stand with regard to their competition.
A competitive advantage can be gained using big data insights and analysis of the competing products and services, based on what customers have to say. An analyst can sift through the big data chunks of information and can make key observations with the metrics and qualitative data pieces presented by the AI-powered platform.
Understand market trends based on customer data
When customers write online posts, whether on social media or in forums, they let companies not only understand what they think about their products but they also form market trends and value context.
Feedback in posts helps businesses identify what is trending and what a possible direction for the company may be. A business can take action if it manages to analyze the data correctly and figure out where its market is headed. So when a business aims to collect huge volumes of individual opinions, it can expect to receive the true customer voice not only about its own products but also about the overall market trends and shifts.
Organizations can then go through a predictive stage, and with the metrics and other analytics tools they have, can take actionable insights about how they wish to move next in their environments. An analyst's work may answer some of the desired market future directions for a company, but it is all based on the customer's insight and voice.
Less guesswork, more reliance on the accuracy
Companies can't base their strategy on hunches or on raw data. They must collect each and every customer comment from everywhere on the open web, let the AI organize such big data, and let businesses figure out the necessary insights in order to make their business evaluations and decisions.
It may be possible to guess what some of the company's customers think, or even want, but in order to collect and measure millions of feedback media posts and points in real-time, a systematic AI-driven analysis becomes a must for every organization.
The more big data analysis takes place, the better companies stay in tune with what their customers need and want, and the greater the chances of such businesses making value choices that would lead them to make better products and services.
Complete your brand picture by adding qualitative data analysis to the quantitative one
Metrics measurements give companies the bottom-line figures on sales and other KPIs items. But behind any number, it is also important to figure out how customers are viewing products and services.
Some key data analytics are not sufficient for companies and do not answer performance parameters or a product's quality issues. If a customer does not like a specific feature within a product, the fact that this product is selling a lot of units now, may not predict its downfall in the future, since the company did not pay attention to a problem created. So knowledge from feedback is always key and could save businesses lots of trouble in the future.
So actionable insights based on data analytics must include both quantitative as well as qualitative main points. While number insights may be easier to look at and analyze, it is the consumer voice about products and services that may answer important product, marketing, and business areas that the sheer figures do not cover.
A business strategy and future growth and success rely on the combination of quantity and quality (customer feedback) understandings. The ability to measure both, read into all of the data analytics context items, and draw the right conclusions, can lead to organizations making actionable moves on all fronts.
The options for taking actionable insights from data
There are 4 options for companies to act upon the data analytics they are receiving, regarding their customer feedback.
Once the business big data has been collected and structured, managers need to think hard on what is the right path to take next, and what is the right actionable insight to take right now.
Taking action can mean different things and may also involve taking an indirect approach, meaning doing things in small increments. Or it can mean even not doing anything at all. So an actionable insight may mean different things at different times for managers.
Since big data is constantly piling up, and analytics takes place all the time, choosing one of the following 4 paths now, does not mean that such a path would be chosen in the next quarter. As business decisions take their turns, today's direction may change completely at a later period of time.
1. Take specific and direct action
The first option offers a clear solution to a specific issue that came up in the customer feedback data analytics. The data presented a customer-specific pain, and when the business understands that such pain is shared by many of its consumers, actionable insight takes place.
A business may also employ predictive analytics, meaning that it uses AI data analysis to identify the chances of future outcomes based on historical data. But whatever the term may be, there is still heavy reliance on figuring out the consumer voice and turning it into actionable insights.
2. Take indirect action (meaning start planning for a later action decision)
In this option, managers may be hesitant to take full action right now. They may opt to take small steps now, wait for the overall picture to become clearer and then make more bold moves.
They can also move around the challenge and deal with related items while leaving the main issue to be dealt with later. Some may use predictive analytics to try and gain an insight or two before deep-diving into the action part.
When choosing this option, managers are taking some action now, but feel that they need to collect additional data before feeling 100% sure about a specific action they need to take. They can focus, for the time being, on related issues and take care of them while trying to analyze other features and insights about their customers' needs and wants.
3. Take no action
Data analytics and consumer big data insights many times lead to no action taken at all. If a business finds itself in a situation where it registers success with a product or a service, there may be no need to take any other action.
Or, sometimes the data collected and analyzed is not sufficient to take actionable insights, and then businesses would opt to not do something. It is also possible that data analytics may mean different things to different managers, meaning that they draw different conclusions from the same data. In that case, they may decide to not take action and wait for more data and more analytics.
4. Reevaluate your whole strategy
The fourth and final actionable insights option is to collect your thoughts, understand that major changes have taken place or are about to happen, and rethink your overall strategy. There could be a crisis happening or a chain of events that can lead to a major strategic change a company chooses to make.
In this case, a deeper big data analysis is usually called for, with a deeper user engagement and customer feedback evaluation performed. Such data collection and analysis can take a longer period of time than usual and could lead to major changes or even a complete overhaul of what the company is doing. It usually also leads to personnel changes.
But even in times of a crisis, relying on market data and customer feedback analytics can still save businesses huge resources and money. There needs to be a reorganization of priorities and KPIs and companies need to define their goals and the ways to achieve them from the start.
Analytics lead to insights and the collective consumer voice, as appears on forums, review boards, direct emails to the company as well as on social media can lead businesses to take actionable steps. There are several examples of taking actions based on customer feedback insights.
The 5 examples of actionable insights
Example 1: Product
Posts in gaming forums give companies player opinions about designs, characters, game challenges, and stages as well as on the technical aspects. While not every player's need and want could translate into actionable insights, the organization can opt to structure such data and categorize the player post texts and keywords.
When a clearer picture evolves from the big data analysis, the company can prioritize and act upon the most requested changes first. A core player post may also require the company to pay more attention to it, and if an AI-powered platform is used properly, some specific posts may deserve and receive significantly more weight.
There are gaming production studios that are releasing many weekly updates to their games, in order to improve all aspects and supply their players with more challenges and fun. Some of those additional features are being put out as a result of player feedback, with the hope that they do please as many players as possible.
Not every player's insight may receive a direct answer, but once changes are implemented and released, players feel that the production people are trying their best to take these games to the next level. This way, players feel that their voice is heard, which can bring them closer to the product and company and increases their game stickiness and retention.
Example 2: Marketing
Marketing efforts, depending on their goals and targets, are measured by the numbers that they produce. But the customer feedback analytics can tell organizations just what users of their products are thinking about the way they are being marketed.
In addition, the overall brand sentiment of a business may rely on how users perceive it, with parts of what they think also relying on their judgment of marketing campaigns.
The knowledge gained from such feedback analytics can tell marketers a lot about making their next actionable insights. Such data helps a marketer to better define future moves and teaches what needs to be done in order to achieve the next quarter's KPIs.
Marketers can also gain lots of insights and knowledge about how paid as well as their organic efforts are being perceived by the customers. Big data analytics of marketing efforts can give new directions to marketers and lead them to actionable new efforts as well.
Example 3: Customer Service
A customer may initiate a conversation online about how he was treated by the customer service department. Another customer will post her thoughts on how much time she had to wait before speaking to a service person. A third consumer would devote his post to the amount of time it took him to collect his refund for a returned good. The chain of customer conversations never stops.
Once businesses are able to collect these insights, appearing on many different internal and external platforms, such big data can lead them to make actionable insights to improve their customer service capabilities.
Organizations may choose to increase the amount of customer service personnel, or will try to implement more automated customer service features. The data tracked can also lead to announcing new goals and targets for measurements of customer service performance.
Another important aspect for businesses is to directly engage with their audience. Users discuss their experiences by writing a post on social media or by sending their thoughts to the business' website. A business representative from the customer service team can then reply directly to the user's post or email, thus showing that customers and their feedback are very important.
Not all post or email insights are actionable, but if the business would handle such feedback by both analyzing it and engaging with the consumer, the customer service measurements would show that the business is truly working for its audience. Such efforts can greatly contribute to the success and growth of an operation.
Example 4: Overall brand sentiment
The ability of big data to show a business how it is performing results in the calculation of positive vs. negative comments and leads to a real-time over brand sentiment score.
Organizations can receive a temperature check of their overall operations by knowing what their brand sentiment score is in real-time. Then they can compare this score on a daily/monthly/quarterly basis to know if customers are happy or not so happy about the business products and services.
Big data thus supply actionable insights on two levels: the micro-level shows each team how they are doing, based on what customers are saying. Then, on a macro-level, the overall performance of the business different teams combines for one general important score, the one of the brand sentiment.
An actionable insight example of the brand sentiment score can be, for example, to find the weakest link or team that is not performing so well and for the business to strengthen and improve its operations. Another conclusion might be that, based on the data and analytics, a brand awareness campaign is what the business needs right now.
Once data analytics leads to actions taken, the business must continue to measure its performance. Such a chain of actions leading to data, which leads to yet more actions, results in measurements such as the brand sentiment score, which tells businesses whether they improved or not.
Example 5: Management
The final example of actionable data analytics relates to the organization's management team. As they stay tuned to what every user is saying, by employing big data analytics, they can make managerial decisions that are more accurate and focused.
Data insights regarding all aspects and features of the business can lead to product, service, and personnel moves, in order to materialize growth and maintain a competitive advantage.
Big data insights can also lead to the adjustment of KPIs, the development of new marketing initiatives, or for managers to make strategic changes in their business plans.
So when a user post appears online, it adds up with many other posts to form a huge chunk of data. The structuring of the raw data and data analytics presentation can lead managers to make actionable insights decisions on all facets of their business.
Affogata enables companies to make actionable insights
In order for businesses to make actionable insights, based on consumer feedback data analytics, a three-point approach is recommended.
Affogata delivers to businesses a variety of data analytics, based on the collection of real-time consumer feedback from all over the open web. By tracking and analyzing the consumer's voice, a single source of truth is created. Organizations find it easier to make valuable business choices by letting their users express their thoughts about products and services.
This three-point method contains a constant loop of businesses taking action, then analyzing how their audience reacts to that, and so on.
Tracking and monitoring of the data
In real-time and all the time, consumers voice their opinions about products and services on many online platforms such as Google product forums, review boards or by writing to the business website directly.
Affogata's AI-powered consumer voice analytics platforms, track all mentions from multitudes of platforms, thus tracking what consumers are saying about specific items and organizations. The platform makes sure to filter out spam and bots, thus having to deal with actual comments and conversations.
It is important to note that the feedback tracking refers to open-web places only, and not closed communities. Still, the amount of mentions in open forums, review boards, social media, and more, is so huge, that enough data is tracked and monitored.
Every post counts and every insight is added to the overall mention count. Such tracking and monitoring serve as a real-time giant online digital survey. Once such mentions’ collection is done, the big data is ready for the next step.
Data analysis that leads to actions
The unstructured data, which contains many different mentions from many different platforms, then go through an organizational process. Taking post after post and many other forms of opinion voicing such as reviews, emails, etc., the AI releases data reports and updates that present new conclusions.
Businesses using such data analytics can then begin to discuss internally how they are going to proceed with their next moves. Such analytics may lead to a variety of decisions, ranging from not doing something at all for the moment all the way up to overhauling totally the business' strategy.
Constant measurement of all feedback
The final point calls for a measurement continuation.
For every action by an organization, there is a reaction from consumers. Be it good or bad, the market never stays static. In order to achieve growth and keep gaining insights and take the next big data consumer-driven actions, the loop of tracking, monitoring, and analyzing consumer feedback must continue.
Every business which already employs data analytics, and then turns it into actionable insights and data-driven decisions, understands the important value such tools and metrics give it. Other operations which are moving slowly towards such digital transformation and usage of structured data are advised to learn the great merits of the big data era and use it for their own good.