Customer Analytics – Page 2

Don’t just look out, look in too
Look-alike modeling for better cross-sell, upsell and growth marketing

“Find me star customers” is just about every marketer’s ask. This is where social media platforms such as Facebook and Data Management Platforms (DMPs) boast of “look-alike modeling” and serve customer acquisition scenarios. Look-alike essentially creates a new audience of people who resemble your existing best customers.

Conceptually, this is how it works – you’ve got your best customers, you derive the defining characteristics of these people, and leverage that insight to get more such people.

Want to get technical? This is how Gartner describes the process.

“So once you’ve built up your model audience, the DMP will look to see what you know about these people – what their attributes are. These attributes are then bumped up against the “internet population” (often), which is simply a model of online adults. By comparing these two groups – model audience vs. internet population – you can see which attributes define your audience and which do not by seeing where they over-index (or under index) vs. the broader population. Then you create another audience using the over-indexing attributes, purchase targets from third-party vendors, and serve them ads.”

Marketers agree, and they get decent returns with this approach.

What is the opportunity then?

A look-alike modeling for customer marketing and growing share of wallet

The above approach so far has been leveraged for customer acquisition use cases only, i.e. social, search and display ads.

How about finding relevant customers for a given marketing goal from within our existing customer base?

Let’s take an example. Direct marketing team is looking to find 3000 people who’d engage with the newly launched organic range. Your current customer base has 45% males and 55% females. The look-alike model will find you the relevant group for the organic range campaign, and you discover that it has 80% females and 20% males (i.e. it is over-indexing on females). Hence, you know that females have a higher inclination for the organic category. For simplicity, this example has only attribute – gender, but in reality, there might be many attributes that are used to create the actual list of 3000 (such as age range, income levels, family size etc.)

This is where the rich first party data within their CDP (Customer Data Platform) is leveraged, to come up with powerful and accurate lookalike lists.

We call this inward audience finding. This is hard because there could be 100s of customer attributes and 1000s of base measures, from which the defining attributes need to be gleaned. The lookalike model we have built, is first of its kind in the industry, and provides marketers the opportunity to find the best target list (as many as you want based on your budget) for a given objective.

Acquiring new customers who are like your star customers is important, but I’d argue that nurturing potential stars within your customer base is non-negotiable. So, don’t just look out for stars, look in too.

AI powered prescriptive segmentation is here
Automated customer segmentation for marketing goals

Wouldn’t it be nice if your marketing tool could help define the target segment based on your marketing objectives?

Imagine the current scenario – you and your data scientist spend days, if not weeks, to figure out the characteristics of customers most likely to respond to a given message, and create the list. What if this could be done immediately?

One of the benefits of having a CDP with clean and connected omnichannel data of each customer is that you can start predicting customer behavior with high accuracy. The challenge lies in defining the variables for modeling. This is the value a data scientist brings to the table. She can look at the data profiles, experiment with different variables, and finally, come up with the most relevant variable(s) to feed into the Machine Learning algorithm.

Given the needs of different B2C verticals vary – restaurants, grocery, fashion, specialty, loyalty programs, e-commerce, there can be an infinite combination of measures and filters that must be evaluated.

Sounds tricky? We at Manthan have automated this process.

Our approach dynamically creates multiple combinations of possible variables for each of the verticals, tests for their efficacy and then select the best ones to build the model automatically. Some applications of this model are in look-alike modeling – which can be used for higher cross-sell, upsell and growth marketing. Details on how this is done are here, in a blog by my colleague.

Another ask we often get is around explainable AI. The users aren’t comfortable with black box AI, and we are glad they seek control and want visibility into how the algorithm works. To a marketer, knowing which behaviors are most relevant is key, so other parts of the business can also benefit from the data science.

Let’s take an example, if the algorithm finds that customers who shop across multiple channels are most likely to become stars in the future, they can craft programs that encourage customers to explore new channels. This insight is critical to business growth.

All of this plays out before the campaigns are even executed. Then, there’s the question of returns on campaign spend – how do you know whether the campaigns were effective? Are you able to prove Marketing ROI to your CFO? To aid this, marketers need extensive testing, experimentation, measurement, and attribution capabilities. But, let me reserve that for another piece….

What do Retailers expect from NRF 2020?

It’s that time of year again!

Retailers around the world are preparing for the NRF Retail Big Show 2020, and once again Manthan speaks to several retail influencers to find out what they are excited about seeing.

This year, Manthan will be talking about Algorithmic Customer Experience at Booth #5747 .

But you’ll love to know that our friends and retail experts are looking forward to everything from sustainability to the snacks being served in the press room!

MANTHAN ASKED:

“What big retail idea do you hope to see at NRF RETAIL’S BIG SHOW 2020?”

“Here we are at 2020, that enchanted perfect vision year that many prognosticated would deliver the future of retail. At the NRF Big Show, expect to see a focus on artificial intelligence (AI) and enablement of exceptional shopping experiences.

I want to see more retailer brands at the core of innovation demonstrations. Greater focus on technology-enabled consumers/store associates as brand ambassadors. Continued innovation around my current three focus areas: AI applied to video, GPS inside stores, and facial recognition.

At the crossroads of new technology platforms and next-generation consumers, continuously expanding ‘retail renaissance’ shopping opportunities will emerge.”

Tony D’Onofrio

CEO of TD Insights LLC
@tonycdonofrio

“Digital transformation and the store of the future are impossible without advanced data networks. Fortunately, broadband is evolving and the rollout of game-changing 5G will support next-generation retail services and in-store innovations.

Look for 5G, SD-WAN, 4G LTE and Gigabyte Class LTE networks to be prominently featured at NRF20.”

Joe Skorupa

Editorial Director, RIS News
@joeskorupa

“I’m looking forward to a fully functioning Jacob Havits Convention Center but as that won’t be the case, I’ll settle for a healthy dose of retail excitement, topped off with a rather fine accompaniment of retail relevance, pizzazz, and downright awesome retail inspiration.”

Andrew Busby

Retail Analyst & Keynote Speaker
@andrewbusby

“I believe at NRF 2020, attendees will see a stronger focus on how sustainable actions are influencing every touchpoint of retail operations. From logistics to inventory management to packaging to customer marketing and more, I believe social good and sustainability, in general, will be a core conversation at NRF 2020.

Additionally, I think that retailers can expect to see more ways in which technology can bring clarity to their operational efforts, connectivity to their customer goals and conversion to their sales.”

Nicole Leinbach Reyhle

Retail Minded, Founder& Publisher
Independent Retailer Conference, Co-Founder
@RetailMinded

“Have retailers finally returned to the need to train their employees to create a branded shopping experience or are they still chasing ways to give more discounts and coupons?”

Bob Phibbs

www.RetailDoc.com
@theretaildoctor

“For 2020, I am looking forward to seeing where personalization can go. We are still just scratching the surface with knowing consumers on an individual level, especially as they interact and shop across channels.

I expect to see more robust opportunities not only for data capture but for analysis and more advance outputs.”

Melissa Gonzalez

Award-winning Retail Strategist
@MelsStyles

“The gap between traditional retail stores & consumer behavior has never been so enormous. Consumers are shifting behavior to invest time & money in more meaningful experiences, so I expect NRF20 to showcase technologies, design and other tools that enable retailers to respond to consumers’ shift from procurement to engagement.”

Diane J. Brisebois

President & CEO, Retail Council of Canada
@LoveRetail

“Two years ago voice was the big thing and last year it was visual analytics taking the limelight so I wonder which of these two will come to the fore in 2020 – or maybe it will be something else.

I hope we will see a little less of artificial intelligence as a lot of the 2019 solutions were rather artificial in an intelligence sense. And I also look forward to the diverse, and plain odd, range of US snack products available in the press room.”

Glynn Davis

Editor, Retail Insider
@glynndavis

“Two things in service of each other: further evidence of the growth of shopping as experience, more reasons to visit; and I’d like to see more momentum behind the race to find much better analytics to identify what experience really means to customers, contextual to their shopping missions.”

Richard Hammond

Author of Friction/Reward
@theseretaildays

From futuristic technology to the latest visionary business cases, NRF 2020 brings together a platform for the industry to showcase what could be. To get a sneak peek of how you could offer your customers an enhanced retail experience through algorithmic programming, visit www.manthan.com/nrf

We hope to see you there!

Better late than never – Salesforce acquires Tableau… 3 years after Manthan-Tableau integration

Manthan is always looking for ways to make data consumption easy and natural for those who need it the most, and are pressed for time. From AI-driven intuitive interfaces to self-serve customizable dashboards, we are constantly pushing the bar to provide the B2C segment insights into their data using our analytics software.

In line with this vision, Manthan partnered with Tableau over 3 years ago to bring together Manthan’s deep expertise in pre-built, industry specific analytics with Tableau’s rich visual analytics.

This week, Salesforce came to the same conclusion, when it announced its intent to buy Tableau. Salesforce’s decision to acquire Tableau highlights their shift from CRM into the analytics field.

Is Salesforce just patching up a flop?

“The acquisition will test Salesforce’s focus”, said Zane Chrane, an analyst at Sanford C. Bernstein & Co. According to Chrane, Business Intelligence is “not Salesforce’s core competency and there is much Tableau does that doesn’t pertain to the CRM world, making the fit slightly imperfect,”

According to this Bloomberg article, the acquisition is also an “implicit admission” that Salesforce’s analytics product, Wave, “was a flop.”

Understanding the drive towards self-serve analytics

Manthan customers can directly access data using the Manthan Customer Data Platform through Tableau and publish dashboards. The solution merges the benefits of strong data governance and pre-packaged data sciences (served by Manthan), and business dashboards (served by Tableau), all in one holistic solution.

Manthan had a CDP in 2012, even before the term was formalized in 2013. Earlier this year, Salesforce recognized its need, and they are now building a CDP.

With over 6 years of serving both business and data science users in the retail industry, and now successfully venturing into the QSR segment, Manthan has long understood how critical customer data management is, and the need for democratizing data and analytics.

Before there was a Customer Data Platform, there was…

…Manthan Customer360, a unique customer data and AI platform to serve as the foundation for personalized marketing. It made the life of the marketer, analyst, data scientist, and IT team easier by helping them organize customer data effectively. Out of the box analytical capabilities directly helped in creating better and targeted campaigns much faster.

Manthan has been a pioneer in the CDP space and has been offering the vertical specific CDP with advanced analytics capabilities to retail businesses globally long before the term was even coined.

Salesforce is behind us on both counts. The way I see it, they’re playing catch up with Manthan.

Let’s not forget the AI-driven power-up

Last year, we added AI capabilities to our Customer Data Platform for B2C enterprises; this enabled the system to constantly upgrade unified customer profiles based on every action, generate astute customer insights and action them with personalized product and offer recommendations.

With Manthan Maya, these insights are made available through an easy conversational interface to business users. This is AI on your desk, and in your pocket on your mobile phone – you ask a question in English, and get the answer, without having to sift through lengthy reports. This means the ability to proactively act on opportunities and respond to market.

Predicting what’s needed for a future-proof offering is something that comes easy to Manthan.

According to TechCrunch, post the Tableau acquisition, Salesforce is now looking into the possibilities of expanding their AI-based initiatives.

Any bets on who Salesforce will acquire next to bring Einstein to life?

Cross channel campaign management – a must for progressive marketers

As a marketer, I understand that customer data management is hard. Add to that varied new and legacy technology stacks, and the challenges increase. Despite understanding what plagues marketing functions, when I am the customer, I get agitated when a business fails to understand my basic preferences.

‘A promotion I receive on text messaging is not accepted on the mobile app, and the customer service team is clueless about it – this is unacceptable!’ This has happened to many of us.

The number of channels continues to grow, and businesses are trying to embrace cross-channel marketing. However, they fail to connect the various sources and channels.

It’s obvious that all marketing needs to be connected, and communicate a consistent message to customers.

The three components to consistent messaging

Creating a unified view of customers across channels
Surfacing insights and identifying marketing opportunities
Orchestrating communications across offline and online channels

Connecting channels with customer data

The most important marketing channels for retail include mobile app, website, email, social, SMS, POS and direct mail. These are critical simply because you must be where your customers are. Marketers also agree that multi-channel retailers outperform single channel retailers, capture a higher share of customer wallet, and their customers have a higher lifetime value.

Download the 4 step CCCM guide

The impact of a Customer Data Platform

A Customer Data Platform forms the foundation of such a cross channel customer marketing platform. Without this in place, marketing orchestration is incomplete and superficial, as you fail to leverage every atom of data you have about the customer, their behaviour, transactions and past responses; and hence know only a sliver of what there is to know about that individual.

At one of the largest multi-format retail conglomerates, this CDP is serving as a strategic asset that teams other than marketing make use of – to introduce new product categories, plan their merchandising and to simplify customer purchase and post-purchase journeys.

Applying Intelligence to Data

Using predictive analytics and Machine Learning algorithms to translate this customer data into insights is where the intelligence lies – what is the customer likely to do next, where is the customer in the lifecycle (new, highly engaged, likely to churn, inactive), how do they respond to promotions, what is their lifetime value and so on.

The benefits of cross channel marketing also lie in building a clear picture of how marketing is driving results at an organizational level, rather than measuring performance at a channel or campaign level.

And this is how I want marketing to influence business and my company’s strategy.

Forrester defines cross channel campaign management as ‘Enterprise marketing technology that supports customer data management, analytics, segmentation, and workflow tools for designing, executing, and measuring campaigns for digital and offline channels.’

Download the 4 step CCCM guide

The six steps to align marketing to customer journey in retail

Plenty has been said about Journey Marketing and it is at the top of the list while evaluating marketing technology today. Rightly so. In a crowded retail landscape, customers have access to options and information from the comfort of their home, and just one below average touchpoint is enough to lose them. Aligning marketing to the customer journey is no more a luxury.

Journey marketing brings a fundamental shift in the way marketers reach out to customers. Marketers use their understanding of the customer, their possible lifecycle stage and purchase paths to create a valuable experience for the customer.

Watch a 2 minute video of how journeys can be configured in Manthan Customer Marketing Platform

This requires work, but let me demystify the process. Here is a step-by-step approach to define and implement customer journey targeting:

1. Create a customer journey map and identify gaps

This should be the starting point for the marketer. A journey map outlines customer’s interactions and experience with the brand across channels over time. It is obvious that this map should reflect the customer’s point of view – for this, step into the customer’s shoes, and don’t be tainted by the brand’s internal process view.

An as-is journey map and an ideal state map should be created. The gap is what journey marketing should help bridge.

2. Set goals for journey stages

Conversion is what all businesses chase, however, this internal goal needs to be translated to customer expectations. As a customer moves across stages, her need for content and information changes. For example, after she has selected the dress, she is looking for the return policy and shipping information.

Brand’s inability to serve contextual content across each stage of the journey will result in losing the sale, and a reduced chance of the customer re-visiting you. Marketers need to define what a customer would expect from each stage, rather than pushing conversions.

3. Each customer is different

This is table stakes for targeted marketing and remains true for journey marketing. Define customer personas and personalize communications based on their preferences. Often, marketing is limited to single dimensional personas, ignoring vast amounts of contextual and interaction information available to a business. Almost all martech vendors today claim to be multi-channel, but in reality, limit marketers with fixed single dimension segments. Omnichannel marketers should make use of all possible data, cluster it across multiple dimensions and create micro-segments to truly understand and engage customers.

4. Automate

Once customer journey maps, lifecycle stages, and personas are defined, marketing is ready to set up automated repeatable campaigns. This converts single-step, single-channel campaigns into multi-step, multi-channel campaigns. Journeys are dynamic, and automation should be able to account for different scenarios. For example, journeys should be able to trigger actions such as changing channels to elicit a better response.

5. Measure

Next, marketers should have the tools to track performance against business goals established for each journey, for example, journeys might have been designed to drive customer engagement, move customers to the next stage or to drive sales conversion. Results from measurement should enable marketers to clearly identify journeys that are effective in driving goals.

6. Optimize

Using data from past performance, and supplementing it up with Machine Learning algorithms can then identify best channels and best offers for a customer. Like anywhere else, personalizing for each segment is the key to maximize engagement, and account for different behaviors. Journeys that embed test & control and A/B testing capabilities are an excellent way to scientifically select the best variation and creatives, and truly understand the effectiveness.

20 technologies impacting restaurants today

According to the Deloitte Restaurant of the Future Survey, restaurant technology is helping QSRs drive conversions and build customer loyalty.

Step into any fast food or quick service restaurant today, and you’ll find technology has impacted everything from ordering to marketing to operations. Aggressive expansion and competition is now pushing the use of technology in this industry even further.

This infographic takes a broad look at all the areas technology is having an impact on restaurants today.

5 Mobile Marketing Tactics Your Restaurant Must Deploy This Quarter

According to reports, orders placed via smartphones and mobile apps will become a $38 billion industry and makeup nearly 11% of all quick-service restaurant sales by 2020.

In 2016, a Nielsen study showed us that millennials are the largest group of smartphone users, as well as the generation that dines out most frequently. In addition to this, the past few years have seen users of all ages become increasingly comfortable with app-based purchases.

In fact, according to research from App Annie, global consumers ordered meals on mobile 130% more in 2018 than in 2016, and worldwide downloads of the top five delivery apps grew 115% during the same period.

It’s no surprise then that many restaurant businesses are eager to explore the various ways they can leverage this booming trend to impact their business.

Let’s see some of the ways this can be done:

Activate users in real-time to drive sales

Advanced analytical solutions can help proactive marketing managers automate their marketing campaigns to nudge users who added items to their cart but didn’t transact. By utilizing abandonment marketing techniques in real-time, such as timely in-app support restaurateurs can help improve conversions on mobile apps.

Taco Bell’s mobile ordering successes are partially attributed to in-app suggestions to guests to create their desired meals, a tactic which many believe is paramount to combatting mobile shopping cart abandonment.

Customer microsegment-based promotions

Using the right analytics, marketing teams can micro-segment customers based on time of transactions and order preferences. This gives the campaign manager pinpoint control over promotional messages to any segment resulting in improved conversions. For example, health-conscious users can be targeted with low-calorie multi-grain pizzas during their app sessions.

Location-based targeting to drive customers

App-driven location data can be used to trigger push notifications to customers based on their city locations along with information on their last purchased items or favorite combos.

Innovative restaurants can leverage location-based targeting creatively to acquire new customers. One such campaign run by Burger King targeted customers within 600-feet of a McDonald’s to unlock a Whopper for 1 cent through its app in December 2018!

Up-sell campaigns to maximize customer value

Cross-selling and upselling can help maximize mobile app purchases, by acting on customer purchase data and responding at the most opportune time. Advanced analytics coupled with marketing automation can provide marketing teams with the ability to deliver rich mobile notifications for upgrades, and add-ons based on customer preferences prior to check-out.

Starbucks entices customers using the app to try out new offerings or upsize their order, with a scrollable news feed that highlights drinks and food with eye-catching images.

Time and event-based personalization

By knowing the customers’ preferred time of dining, offering push notifications with deep links can help drive purchases. Advanced analytics can help leverage this customer insight to understand peak times and customer drivers, into to drive sales with timely messages.

The Mobile Restaurant Future

According to DMI research, the mobile activities most desired by diners include viewing menus, finding the closest locations and placing orders. Restaurants which have the right tools in place can, therefore, leverage the power of mobile technology to increase conversions, build loyalty and improve their market share.

10 Ways Analytics is Changing Restaurant Business

Restaurant owners are sitting on a ton of data – from employee information to mobile apps, supply chain logistics to touchscreen kiosks, e-commerce numbers to social media reviews. BCG reports that four out of five restaurant brands can access a wealth of data from multiple sources, however, only one in five is using that data comprehensively.

If you’re in the restaurant business, you’ve probably already heard a lot about how restaurant analytics can help bring in more customers. But are you aware of the operational, and predictive ways in which analytics can impact other areas of your business?

Sales: With advanced restaurant business analytics software you can uncover key insights on the performance of stores and products, the productivity of resources, and sales category performance – all in real-time. If sales are down during the first part of a Saturday, knowing that dip can help you send out offers to improve the day’s performance immediately – as opposed to seeing the report on Monday afternoon when it’s too late.
Operations: A direct impact on your business is the operational efficiency. Use data analytics for restaurant to see how you’re doing on key operational metrics such as delivery time, cook time, wait time, store efficiencies, labor, and more. Know the performance of franchise owners by region and/or store.
Menu: You may already know which menu items are selling the best or doing the worst. But advanced restaurant software can also give you insights into deeper questions such as which products sell well together, what are the top customer choices by demography, how historical sales patterns will impact transaction value and much more.
Location: Can data analytics for restaurant help you find the best place to open a new store? Yes, it can! Analyze the potential of a new store location, using location and guest demographics to postulate footfalls and potential sales. Compare and choose the best site for your restaurant with comprehensive data sets that translate into high demand.
Mobile App: Improve engagement on your mobile app through in-depth analytical understanding. Know when, where, and how customers use your app in order to engage them with contextual messages in real-time. Encourage mobile app orders based on location, current order, cart components and the guests’ historical relationship with your brand.
Customer Satisfaction: Advanced restaurant analytics software should be able to give a view of customer satisfaction by channel and store, allowing deep dive analysis of how Net Promoters Score (NPS) impacts store performance. Using analytics, you can, therefore, identify both broad (across-store) and narrow (within a store) reasons for dissatisfaction down to each guest level.
E-Commerce: Analytics enables a finer understanding of online customer purchase behavior, a source of traffic, traffic conversion, preferred offers, typical order size and more. Using this information, you can then influence customers as they place orders by recommending relevant products they might like and minimizing abandonment with timely intervention.
Marketing: Restaurant Software enables clarity into campaign performance and its impact on sales. By harnessing customer data from various source systems, you’ll be able to understand your campaign performance from the guests’ context, and deliver personalized 1-1 campaigns on channels they prefer: during the best daypart, on a day they are likely to respond; all using automated marketing.
Personalizing: Your frequent guests and your at-risk customers are those with the biggest impact on your bottom line. Analytical behavioral clustering, propensity models and churn prediction algorithms can help you uncover customer opportunities and risks. You can dynamically segment customers on multiple dimensions such as day-part, order value, visit frequency, price sensitivity, taste, occasion preferences and more. By knowing the micro-segment each customer belongs to, you can tailor your messages with specific promotions, reduce churn and give loyal customers more reasons to return.
Compliance: Maintaining brand, safety, and employee training standards across equity and franchise stores is critical. Advanced analytics can help ensure operations and food safety compliance, monitor status of employee training, identify talent and understand the impact of employee performance on store performance.

Download Guide to Informed decision-making in restaurant business

Go Beyond Reporting

The sophistication of analytics has now evolved beyond mere day-to day-reports. By bringing together disparate systems, and applying the right advanced analytics solution you’ll be able to uncover the hidden meaning behind all that data.

A savvy restaurant entrepreneur can leverage these analytics to make better decisions regarding operations, campaigns, customers, and strategy, to outthink the competition.

Articles in Category: Customer Analytics

Look-alike modeling for better cross-sell, upsell and growth marketing