Brands that evolve their loyalty strategy to create more personalized, engaging experiences for their consumers are the ones who will thrive in the new, more fragmented marketing landscape.

Data-driven loyalty strategies allow marketers to drive impact at scale and meet the ever-shifting needs of their consumers and the business. This is a time to innovate and develop mutually beneficial brand-consumer connections that will stand the test of time.

Customer data is valuable; however, making sense of data is much different than simply possessing it.

Customers leave a trail of breadcrumbs – data like demographic details, online/offline purchases, website visits, email opens, and more. The pain point remains combining these disparate data sets and connecting them to the representation of particular customers. Anticipating consumer value and taking actions to nudge them through a personalized journey starts with a holistic view of your customer via data points that can paint a picture. Besides first-party data, this may include transactional data on competitive and channel spending and category purchases, according to Shivkumar Ananthakrishnan, Director of Strategic Consulting at Epsilon APAC and MEA.

The volume of data is constantly growing, and while it is accessible in real time, brands still struggle to unify such disparate data and make actionable insights.

Unassembled jigsaw puzzle pieces don’t make sense, but they reveal a complete picture. Data must also be integrated to paint a 360-degree view of the customers, combining qualitative, quantitative and predictive behaviors.

The first step is identifying various data owners and sources across the organization, analyzing whether the data outputs are relevant, and the scope for optimization.

The second step will be crucial, where brands must create a road map to meet their future data objectives, including decisions like technology and personnel.

The third step, and often the toughest, is to create an action plan for putting the connected data to work to make the brand’s loyalty program connect with their customers financially and form a personal connection with them.

Aim for a unified, marketing-optimized view of every consumer. Linking the bits and bytes of customers’ actions will help realize the value of the loyalty programme’s members and their preferences. Poor data utilization eventually impacts member value understanding and personalization, raising the possibility of attrition due to inadequate interaction.

Using tech to drive business

Making consumer experiences frictionless and engaging justifies why firms invest enormous sums of money in creating machine learning models. However, Gartner says that through 2022, 85% of machine learning models will fail to provide the desired outcomes.

Marketers must understand that business requirements should drive their data management systems and procedures instead of letting technology dictate their customer strategy. There should be a focus on ensuring proper channels for the consumers to voice their opinions, listen to them, and make adjustments wherever necessary. Keeping existing customers is considerably more valuable than acquiring new ones. An intelligent Customer Data Platform like Epsilon’s PeopleCloud Customer combines all the data, makes it accessible at scale, and can power real-time recommendations that can be activated across channels.

Loyalty programs must look for methods to put members in charge of their experience while delivering true moments of gratitude and recognition. Customers want real-time, meaningful, and personalized loyalty connections across their life cycles.

Maintain financial efficiency

While running a loyalty program, marketing teams look at two financial objectives: optimizing the cost of the loyalty program and driving more revenue. Unlike popular perception, one does not have to be at the expense of the other. Member engagement improves when first-party data is efficiently used to deploy the right personalization strategies. This sets the tone for reduced Cost Per Point (CPP).

The balance that brands need to find is not between the cost of the loyalty program and the revenue targets. It needs to be between revenue goals and customer delight. Today’s Platform capabilities can set up brands and customers for success, creating a win-win for all.

To conclude

The best loyalty programs may increase annual revenue from consumers who redeem points by 15 to 25% by boosting either their frequency of purchases, the size of their baskets, or both, as per research by McKinsey & Company.

All loyalty programs have members who don’t use their points, leading to their expiration. This “breakage” usually lowers a programme’s liability on its balance sheet, which is good news for program economics but can lead to high customer dissatisfaction. Brands need to design programs that give consumers compelling reasons to redeem their points and gain loyalty benefits. But that only sometimes tells the whole tale. Breakage can also occur for tactical and entirely avoidable reasons, such as difficulty in redeeming points, participants forgetting they are registered in a program, unappealing or irrelevant prizes, and impractical or high reward levels.

Loyalty programs must overcome obstacles and demonstrate their value. Loyalty executives can increase program value and delight their customers and businesses by knowing the levers that advance them and optimizing their value.

The post Personalized Experiences Key to Success in Fragmented Landscape appeared first on MartechView.

Facebook, Twitter and Instagram are the top three most visited social media platforms after Google and YouTube. Social media platforms are a fertile ground for brands to advertise, engage with their audience, and add new consumers to their bucket.

But what really drives engagement over these platforms? The answer is content. An algorithm is a set of mathematical rules describing how data collection should behave. Social media algorithms help maintain order and rank search results and advertisements. For instance, an algorithm on Facebook directs the order in which the content is displayed.

Let us understand the algorithms working behind the top social media platforms.

Facebook

Every time someone checks their Facebook feed, the posts they see and the order in which they appear are determined by the algorithm. Considering the volume of users on its platform, it is nearly impossible that Facebook’s ranking system relies on a single algorithm. Instead, the team has to apply multiple layers of machine learning (ML) models to predict the most relevant, meaningful and engaging content for its individual users. The Facebook algorithms follow four steps process, namely:

Step 1: The step looks at the inventory – all the content available on the platform. It then considers the likes and dislikes, posts from friends, and groups one has joined.

Step 2: Next, it moves to consider thousands of active and passive signals. Active signals are likes, comments, shares, and replies measure engagement. Passive signals include view time, the time of posting, internet connection, etc..

Step 3: Then comes the prediction stage. Here, the algorithm uses thousands of signals to determine the engagement rate with a particular post. Say, for example, the likelihood of a user commenting, how much time they would engage with the post if the user watches the entire video, etc.

Step 4: In the final step, the algorithm will assign a relevancy score to a particular post, i.e., how much interest people will show to a particular post. Once the relevancy score is determined, the content is distributed.

Twitter

Twitter offers its users two different timelines: Home Timeline or Latest Tweets. A real-time chronological timeline of Tweets from the people you follow is displayed in Latest Tweets. Home moves posts into what it thinks is a better order using its ranking algorithm. Twitter’s ranking algorithm uses multiple ranking signals based on:

Recency: Depends on how popular a topic is, rather than looking for popular topics on a daily basis or for a while.

Relevancy: Users’ engagement history, from the posts they like, and topics they follow, to even the trends going viral at their location.

Engagement: This one takes care of the popular tweets, how people in one’s network react to those tweets, to like, retweet, etc.

Rich media: Depending on users’ interaction, images, videos, GIFs and polls get into the feeds.

The truth is that although the platform uses machine learning, not even Twitter is certain of the exact results of its algorithms. As part of its “responsible machine learning initiative,” Twitter is now investigating the outcomes of its algorithms.

Instagram

As Instagram describes it, there are multiple algorithms at play — each one for a different part of the platform, whether it’s feed posts or Reels. The algorithms take the users’ preferences, termed signals, to create content that helps users to remain hooked to the app. Hence, thousands of signals across four critical aspects (in descending order of importance) come together for a personalised view, which includes,

About the post: This one considers how well-liked a post is, as well as less important details about the content itself, such as when it was posted, length of a video, and location.

About the person who posts: This step allows the algorithm to get a sense of how interesting a person can be for a particular user, which includes signals like how frequently individuals have communicated with that person over the past several weeks or how well they have responded to their content.

Users’ activity: The task here is to find a particular user’s likes and dislikes, and understand the posts they like the most depending on what they share.

Users’ interaction history: This helps the platform understand the users general level of interest in reading posts from a certain person. Take, for example, whether they leave comments on one other’s posts.

Beyond the interest score, various other variables may affect where a post appears in a user’s home feed. These include the quality of the photo or video, the originality of the post, the violation of Instagram’s community guidelines, or if the content is reported.

TikTok

The TikTok algorithm makes personalized content recommendations for each of its users. Based on the specific tastes of each user, it chooses which videos to show them on their page. The algorithms take several signals into account, which include,

Behavioral signals of users: TikTok takes many factors into consideration, but the most crucial one is how users act on the platform. With the help of data such as video likes and shares, comments made, accounts followed, and the content created – the platform can better comprehend each user’s distinct tastes, and how they evolve over time. Based on how closely these behavioral signals match with the films, it ranks the clips.

Content discoverability: TikTok learns how to rank the content by using information from the video itself, such as captions, hashtags, and sounds. This is mostly because it uses the data to determine what the content is about, and when to display it on users’ pages.

Account indicators: TikTok analyzes each user’s device and account settings to maximise performance. It primarily considers country choices, preferred languages, device kind, category choice, etc. However, they don’t carry as much weight as the other two criteria because people don’t express them as preferences.

LinkedIn

LinkedIn has its own algorithm that can connect users, identify jobs, surface relevant material, and surface any other information a user would be looking for on the platform. The LinkedIn algorithm prefers relevance over recency. Its objective is to present readers with the information they are interested in, before more current updates. This enables LinkedIn to show users the articles, videos, jobs, and other content that matches their search criteria or are most likely to find interesting. The signals the LinkedIn platform uses include:

Connections: The algorithms consider connections as a reference to identify users’ tastes and preferences. More connections on LinkedIn equals more possibilities to rub shoulders with other professionals digitally. This allows people outside one’s connections to see their posts and vice-versa. Similarly, the LinkedIn algorithm considers the users one has interacted with through comments, likes, shares, and responses.

What is the content: The algorithm used by LinkedIn determines how pertinent a user’s content is to your interests. It “reads” the hashtag, post, page, and profile kinds users have interacted with most frequently. Again, based on users’ prior online activity, LinkedIn may even provide users with content unrelated to their current connections.

Engagement: The likelihood that a user will interact with further content is assessed by the LinkedIn algorithm using recent likes, shares, comments, Connection requests, and other engagement indicators.

To sum up,

An algorithm’s job is to provide users with pertinent content. It is used to filter through the vast amount of content available on each social media network. On the flip side, there is a call for large social media giants to shift towards ‘Explainable ML models’ for justifiable and transparent decision-making. What’s your take?

The post Unlocking the Mechanics of Social Media’s Engaging Algorithms appeared first on MartechView.

Contact centers remain at the front line of customer support. Hence, to effectively address frequent contact center issues, staying on top is imperative. Moreover, contact centers are difficult places to work – increasing costs, agent absenteeism, and turnover while lowering the quality of the customer experience, team morale, and efficiency.

This scenario needs to be taken care of, giving rise to the need for a system that can record and keep track of relevant information about customer issues that need managing. The Ticketing dashboard helps you achieve this. In addition to providing customer care representatives with a tool to log calls, ticketing systems assist calls to be handled more quickly and with less waiting for the correct person to receive a ticket.

Keep your contact centers in order

The customer support team in any contact center needs to prioritize issues in descending order of importance and organize its day-to-day workloads. Reps can use a ticketing system to swiftly search archived cases and get crucial historical data instead of hunting down an old email thread or call tape.

Given the importance these ticketing dashboard systems hold, it becomes crucial to delve deeper and look for some of the best ticketing tools or software available at the disposal of various enterprises.

Ticketing software from Hubspot – Service Hub, which manages tickets on a single dashboard, is good to start with. Reps have access to the ticket’s history of interaction and can follow the case’s development as it goes through troubleshooting. The software helps connect a customer’s contact card with the CRM, making it easy to look up past interactions.

Service Hub provides ticket analytics, helping track important indicators like agent response time and ticket volume. This add-on feature helps service managers track work effectiveness and show whether a team can satisfy consumer demand.

Zendesk Ticketing system is another tool offering numerous ticket management features. Its ticketing system automates common and repetitive tasks, gives agents access to the history of previous customer contacts, and incorporates all client communication into the platform. It supports automatic ticket production, which creates tickets from calls, messages, and texts without the involvement of any individual agents.

Additionally, the tool provides pre-built dashboards to provide visibility into performance metrics of cases registered, resolved, and time taken to resolve, among others. This eventually helps find the gaps and suggest ways to improve business efficiency.

In supporting the customer service department, AzureDesk’s ticketing system comes next. It provides capabilities like linking an infinite number of email addresses with the software. This may be financially sensible for teams with a significant customer service workforce. Moreover, AzureDesk provides easy integration with other customer service applications like Slack or JIRA. 

The flexible API structure of its ticket system makes it compatible with most customer service integrations. This is helpful for businesses that want to give their customers an omnichannel experience.

SupportBee gives complete customer support staff access to a single, centralized inbox where it is easy for everyone to see client messages, issues, and complaints. It also offers limitless inboxes, tickets, and customer satisfaction scores. Their knowledge base software integration enables clients to assist themselves. Customers can skip writing to customer support to get solutions to their questions.

Similarly, the customer portal software from SupportBee gives clients a dedicated space to handle and follow the status of their support requests. 

We can end the list with the Zoho Help desk ticketing system. It helps collect tickets across channels, including social media, email, live chat, web forms, and telephone. It is easy to make assignments based on predefined criteria and automatically assign tickets to customer support representatives.

A sentiment analysis tool enables agents to assess the tone of a ticket before opening it. Reps may respond to negative tickets quicker, preventing customers from venting their frustration.

Attached benefits

The ability to collect and arrange all incoming support inquiries is one of the most obvious benefits, particularly for contact centers handling multiple inquiries. In addition to making it simple for agents to manage support issues, it greatly reduces the likelihood that some client requests would go unaddressed or unresolved.

Ticketing software help consolidate all customer communications (across different channels) into one unified thread. This implies that customers can use a preferred channel or switch channels while resolving a problem. The exchanges will also always be recorded in the same location.

Additionally, ticketing systems encourage greater cooperation between customer support team members, as it provides a variety of collaboration tools like transferring and escalating tickets to senior team members, adding private notes to tickets that are only visible to agents, internal messaging capabilities, and more. These ultimately lead to increased agent productivity and efficiency, reduced opportunity for human mistakes, and quicker ticket response times.

Customer satisfaction will inevitably rise as ticket backlogs are decreased, interactions are professional and personalized, and consumers receive better service.

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