When it comes to marketing analytics, LatentView has possibly the largest body of work and experience. We are recognized as leaders in Digital Marketing Analytics and often work as exclusive partners to our customers in this area. Our Big Data expertise allows us to look for insights in large, complex digital data from multiple disparate sources. Our insights have helped our customers achieve:
As companies are increasingly forced to expand their digital footprints, there lies a huge opportunity for marketers to use customer analytics to generate superior consumer insights previously unavailable to their traditional counterparts.
Social media, for example, offers a large panel of consumers and the opportunity to listen-in to natural conversations that are not influenced or boxed in any way. This gives marketers the opportunity to understand consumer preferences and consumption patterns, faster and on demand. It also exposes them to a greater depth and breadth of conversations, providing them with both valuable context as well as unexpected relations and preferences that could influence an entire marketing campaign.
Combining internal and external data as well as structured and unstructured data, provides marketers a greater granularity into consumer behavior that maintains relevance even with scale. When structured CRM data is combined with unstructured geo-location data, marketers can customize campaigns at an individual consumer level.
LatentView helps marketers achieve a competitive advantage by providing them with a 360 degree view of their consumers.
Purchase Driver Analysis
Behavioral Segmentation
Social Media Listening
Micro Targeting
Conversations were mine using a proprietory text mining tool to understand consumer perceptions and associations with the brand.
Relevant conversations were extracted using a proprietory text mining tool and were cross-analyzed to understand usage patterns and related behaviour.
With increased competition and decreased consumer attention spans, marketers need to ensure their messages are customized and relevant to each and every consumer segment. By using data, marketers can micro-segment their consumers to increase campaign success.
In addition, by using data for segmentation, they are not limited to the more traditional demography-based segmentation and can combine it with psychographic segmentation that provides them insights into affinities and preferences.
Data analytics can also be used to ensure timely delivery of relevant content. Geo-location data combined with search data, can for example, ensure a consumer receives an offer on “just what they need” as they approach a 1 mile radius of a specific retailer.
LatentView helps marketers measure and improve the effectiveness of their marketing campaigns across channels through effective targeting and segmentation, content match-making and testing.
Campaign Monitoring
Propensity Modeling
Social Buzz Monitoring
Uplift Modeling
Using Test Vs Control methodology to develop a simple look-alike analytical model to predict if a 5% cash-back offer would result in a positive ROI over a period of 1 year.
Marketers can improve the effectiveness of their programs and see increased returns on their spends, by using marketing analytics to understand their consumers’path to purchase and the interplay between channels- online and offline. This in turn equipsthem to better influence shopping behavior.
In addition to doing this, insights from data can also help them understand what the optimal media mix is for a specific product or service – which channels are working, what should the optimal spend be on each channel as well as which channel should be used for a desired call to action.
LatentView’s Integrated Marketing Mix Optimization (IMMO) suite consists of advanced, automated solutions that enable timely and granular insights that can significantly improve the overall effectiveness of any marketing program.
The IMMO suite helps us provide Marketers with answers to the questions that will have the greatest impact on Marketing performance.
Media Mix Optimization
Social Media Influencer Analysis
Attribution Analysis
Purchase Driver Analysis
Models built with traditional transactional data are not accurate in a digital world. So, consumer sentiments and online engagement was tapped, digital broadcast data was analysed to improve revenue forecast accuracy and optimize promotional spend.
A media mix model was built for a leading department store to optimize marketing spend across multiple channels.
Some of the more advanced marketing organizations today are turning to digital data to identify trends that are going to be the next big thing, before they actually are. This gives Marketers a huge first mover advantage when it comes to developing and launching products for which a ready market exists.
Digital data analysis can also help Marketers understand usage patterns and look for unaddressed gaps where they could innovate. Mobile data and web data can give us clues into consumer usage that consumers themselves are not aware of. When are they using it?How are they using it?What are the challenges they face? These are all questions that can directly inform a product innovation process.
For an online payment solutions company, an exploratory analysis found that consumers needed to make a specific number of transactions in a given time frame in order to avoid lapses and convert more loyal consumers. The key then was to encourage them to use the money in the payment gateway, and from that finding, a new gifting service evolved.
Big Data offers the ideal environment to run “test and learn” experiments. Consumer behavior can often seem erratic and unpredictable and in these situations, test and learn method of data analysis provides answers to seemingly unanswerable questions- to find patterns where none have been found before.
Purchase Driver Analysis
Early Warning System
Social Media Listening
Ab Testing
Social Media Listening was used to identify the next BIG snack for a global food & beverages giant
Exploratory analysis uncovered an insight that would lead to an entire new business offering and channel for customer acquisition
In the halls of marketing fame, customer acquisition stories get all the attention while those involving retention are rarely discussed with the same fervor and enthusiasm. Yet, most marketers will admit that customer retention is an extremely important part of their strategy and one that impacts the bottom line in a significant way. According to global consulting firm, Bain & Company, it costs six to seven times more to acquire a new customer than to keep an existing one. We provide superior consumer behavior and customer analytics to enable higher retention.
Personalization – of products and experiences – has been described as the route to building loyalty, but in today’s multi-channel world, creating that customized experience is complicated. Marketers need to combine behavioral data like website visits, bounce rates with other critical indicators regarding attitude and sentiment toward the brand, in order to get a better understanding of brand affinities.
Consumers move through stages of purchase. LatentView can help marketers identify what stage of purchase a consumer is in, provide them with a customized shopping experience, measure consumer satisfaction and identify pain points. Its Early Warning System can even flag off consumers who are likely to become a churn risk, before they actually do, allowing marketers to invest in retaining that consumer.
Customer Pain Point Analysis
Omni-Channel Consumer Mgmt
Voice of Customer
Pricing & Features Optimization
Identified four areas that would have greatest impact and used demand prediction and advanced diagnostics models to optimize service times
With solutions like DigiView making it possible to measure the impact of marketing on sales, advanced marketers are now using data analytics to devise strategies that have a direct impact on the revenues and marketshare of a product or brand.
By combining internal and external data effectively, multiple customer touchpoints can be used as channels to cross-sell and upsell. Effective recommendation engines serve to address consumer needs effectively, thereby increasing the revenue per customer.
Through better targeting and segmentation, high net-worth individuals and ideal consumers can be identified early in their purchase cycle.
Data analytics also allow marketers to take into consideration the lifetime value of a customer and increases the number of consumers that move up each stage of loyalty.
Market Share & Revenue Forecasting
Cross & Up – Sell Analytics
Customer Lifetime Value Analysis
Market & Competitor Intelligence
A Fortune 500 CPG company wanted to increase revenues while building relationships.
