Holistic Approach applies not just for customer management strategy, it also applies to digital marketing execution and delivery. Good strategies are materialized into execution phase in data-warehouse, software development, spatial data enrichment and digital marketing materials. These requires specialized knowledge and we partnered with best in class partners in their field.
Marketers holy grail is to find right answers of basic questions of targeting: To whom, what to offer, when to offer at what price. A popular saying illustrating how difficult it was to qualify the response to advertising is attributed to John Wanamaker:
“Half the money I spend on advertising is wasted; the trouble is I don’t know which half”.
According to our experience X-Sell campaign response rates are actually much lower. Vast majority of campaign response rates -not supported by AI (machine learning) models- are between 1%- 5% depending on industry.
One of the most common goal of our AI projects is to increase efficiency of X-Sell campaigns. In such projects we do analyze data from past campaigns and build response models or in case of campaign-based analysis is not possible, we build look-a-like models based on portfolio overlap.
Then we do score customers based on their affinity of given offer. We build profit charts and optimize campaign audience based on customer affinity score, cost per targeting (call, email), estimated revenue from successful X-Sell.
We apply test and control methodology to validate model and campaign success results. Results are highly dependent on industry, market environment and available data quality.
In our projects we have seen 2X-6X higher response rates increase for X Sell campaigns where audience selected based on Machine Learning models versus control group.
With our solution partners we also help our customers to integrate AI results into their core BAU processes via automating entire process.
Next Best Offer (NBO) or Next Best Action (NBA) machine learning models primarily supports outbound channels, email, call center optimization. It helps marketers to resolve evergreen question of sales and marketing. What to offer / X-sell to whom? With our ML, AI solution our clients were able to save call center costs as well as boost X-Sell teams efficiency. At the same time our clients improved customer satisfaction levels thank to tailored, relevant offers to customers.
We develop custom machine learning models that is tailored for your business and learned on your data. Depending on database availability but most of the AI use-cases described below can be accomplished within a few weeks. Our process is based on Cross Industry Standard Process we add best of agile framework to it make sure we get the highest. The following iterative steps describes 80% of the AI project lifecycle.
In this phase we define together business goals to be achieved with AI / ML , Understanding how a potential AI / ML model can outperform existing status quo process. What is the baseline performance and how we will measure success.
We need to map internal end external data that can improve model performance. It requires a slight of business process mapping from data perspective and some tradeoffs might be made based on the nature of data (availability, price, quality, time to extract). There is no need to have a fully fledged DWH or data lake to get a successful an ML project. (Who has ever seen a complete Data Warehouse or Data Lake?)
Our experts have trained over 100.000+ Machine Learning models in 100+ projects. Lot of expertise have been accumulated. We also apply our custom AI to tune ML model parameters to get the best possible results for your business.
Depending on the ML use case we do validate the models on multiple levels before we let the model published for test campaign.
Deployment of approved models might require some DEVOPS MLOPS both on “caller” and model “serving” side. Serving will depend on the use-case a REST API or batch processing is required.
Onsite machine learning recommendations are somewhat different from offline next best offer as they need to process real time session history on top of customer order history. This solution also handles cold start challenges as for new customers we have significantly less information about customer.
One of the biggest challenge for most of marketers across industries being it retail, e-commerce what service or product to offer on the next campaign or at next customer touch-point being inbound or outbound. Common approach especially when potential offer palette is relatively small to go for highest revenue regardless customer need.
In a more matured and advanced stage organizations tend to fine tune their campaigns based on earlier results with descriptive or predictive models as described in chapter “Next Best Offer”. That can lead to strong results as long as the offer palette is a few dozen.
We do help our customers with combined AI algorithms to find next best offer that is personalized to the customer level even when there are hundreds thousands of customers and products varieties are over ten thousands threshold. Our solution combines geo-demographics with collaborative filtering and content based filtering to find best offer for the customers customer.
For ecommerce you can find off the shelf tools with built in recommendations. That are limited to common fields and features. Off the shelf recommenders are better than nothing, however in most of the cases you have more data that has a significant impact on model efficiency, that is why larger ecommerce sites go for custom built recommendation engines on the top of TCO and ROI considerations.
With our tailored recommendation engine we managed to triple click thru rates in e-commerce.
Read more about our customer story how we boosted with recommendation engine email Open Rate and Click Thru Rate at Netpincér.
Primary objective – from analytics perspective – of churn management projects is to identify high probability to churn or attrition segment within Portfolio. While we build machine learning, churn predictive models we do identify and share with the customers key drivers of attrition, churn. We also recommend and implement action steps based on findings. We build descriptive dashboards to measure Churn Management Performance based on saved customer value.
Customer whereabouts Geospatial information such as home address, frequent path, transaction location can significantly increase targeting accuracy. With our GIS partners we can geocode and enrich customer attributes with public GIS information such as income proxies, property price index, population density, population demographics, location surrounding residential type, town center index, tourism index and couple more that boosts machine learning models accuracy especially when customer level data availability is limited.
Combining GIS public information and our semi automated AI approach helped us to predict which customers will visit which bank branch how many times just within 6 days.
AI combined with GIS data not just greatly improved efficiency of cross-sell campaigns of our customers but significantly boosted offline Door to Door and billboard campaigns efficiency.
On top of location based targeting with GIS and AI Holistic and GeoX consultants can support business development where to open next branch, restaurant, kiosk. Where they can expect the most customer visit from their preferred segments. We do analysis based on historical performance if it is available, if past performance data is not available for given business we can analyze and predict best performing locations based on competitive environment location analysis.
Machine Learning can help with sophisticated answers for most of core questions with or without data from a Big Data source. We have achieved up to 6 times response rate increase via ML driven campaigns compared to control group only on data from core and CRM systems without any big data source. We have achieved 30% response rate via combining Big Data event driven campaign solution with AI.
Contact us for more details: firstname.lastname@example.org
Our consultants have decades of cumulative experience in AI, we have built 100+ machine learning models across multiple industries from banking to e-commerce across multiple geographies.
X–Sell helps businesses upsell and cross-sell products to customers. It uses machine learning to recommend products to customers based on their past purchases and browsing history. Machine learning driven X–Sell can help businesses increase sales and revenue by recommending products that customers are likely to be interested in.
It can also help businesses improve customer satisfaction by recommending products that meet customer needs. Machine learning models can boost X–Sell process in various ways. Finding the right time to present the offer.
1. Automated machine learning can help identify the right time to show an offer to a customer. Machine learning models can analyze customer data to find patterns in customer behavior. This information can be used to determine when a customer is most likely to be interested in a product.
2. Machine learning can also help identify what products to recommend. Machine learning models can analyze customer data to find patterns in customer behavior. This information can be used to recommend products that customers are likely to be interested in.
3. Machine learning can also help improve the accuracy of predictions. Machine learning models can be trained to predict customer behavior. This information can be used to improve the accuracy of predictions made by the X–Sell system.
4. Machine learning can also help improve the efficiency of the X–Sell system. Machine learning models can be used to automate the process of making recommendations. This can help reduce the amount of time needed to make recommendations.
5. Machine learning can also help improve the scalability of the X–Sell system. Machine learning models can be used to make recommendations to a large number of customers. This can help businesses scale the X–Sell system to meet the needs of their customer base.
Customer experience optimization with AI and Machine Learning involves using AI and machine learning techniques to improve various aspects of the customer experience. This can include things like personalizing marketing and sales efforts, improving customer service, and identifying and addressing customer pain points.
Some specific techniques that can be used include natural language processing (NLP) for chatbots and voice assistants, predictive analytics for identifying potential customers and tailoring messaging, and machine learning algorithms for analyzing customer data and identifying patterns and trends.
Personalized product recommendations: Retail companies can use AI and machine learning to analyze customer data such as browsing and purchasing history to provide personalized product recommendations. This improves the customer experience by giving them relevant and tailored options, increasing the likelihood of a purchase.
Predictive customer service: AI and machine learning can be used to predict customer inquiries and complaints, allowing companies to proactively address potential issues. This improves the customer experience by providing fast and efficient service.
Real-time chatbot assistance: AI-powered chatbots can be integrated into a company’s website or mobile app to provide instant assistance to customers. This improves the customer experience by providing quick and easy access to support, and reduces the need for human customer service representatives.
Automated marketing campaigns: AI and machine learning can be used to analyze customer data and behavior to create personalized marketing campaigns. This improves the customer experience by providing relevant and tailored offers, increasing the likelihood of engagement and conversion.
Fraud detection and prevention: AI and machine learning can be used to analyze customer data and detect potential fraud. This improves the customer experience by providing a secure and reliable service, and protects customers’ personal information and financial data.
Product recommendations: eCommerce sites can use machine learning algorithms to personalize product recommendations for customers based on their browsing and purchase history.
Inventory management: Retail companies can use machine learning algorithms to predict demand for products and optimize inventory levels.
Price optimization: Retail companies can use machine learning algorithms to optimize prices based on demand, competition, and other factors.
Fraud detection: Retail companies can use machine learning algorithms to detect fraudulent transactions and protect against losses.
Chatbots: Retail companies can use machine learning algorithms to train chatbots to assist customers with queries and support.
Image recognition: ecommerce sites can use machine learning algorithms to identify products in images and improve search capabilities.
Customer segmentation: Retail companies can use machine learning algorithms to segment customers based on demographics, purchase history, and other factors to create targeted marketing campaigns
Sentiment Analysis: Machine learning can be used to analyze customer reviews and feedback, helping ecommerce businesses understand customer sentiment and make improvements to their products and services.keting campaigns.
Product forecasting: Retail companies can use machine learning algorithms to predict future demand for products, allowing them to plan inventory and make strategic decisions about new products.
Most of retail AI usecases are applicable for banking there are some specific usecases where machine learning can add value to FSI industry
Credit scoring: Machine learning can be used to predict the creditworthiness of individuals or businesses, allowing banks to make more informed lending decisions.
Risk management: Machine learning can be used to analyze market trends and predict potential risks, allowing banks to take proactive measures to mitigate these risks.
Automated underwriting: Machine learning can be used to automate the process of underwriting loans and insurance policies, reducing processing time and increasing accuracy.
Investment management: Machine learning can be used to analyze market trends and develop investment strategies, helping banks manage their portfolios more effectively.
Financial forecasting: Machine learning can be used to predict future financial trends, allowing banks to make more informed decisions about investing, lending, and other financial activities.
We can help implementing AI for the following use-cases:
• Create Strategy for AI/ML driven digital transformation
• Build and deploy AI marketing models on customer data
• Build and deploy AI marketing models on sales and marketing data
• Use AI to improve customer acquisition and retention
• Use AI to improve customer satisfaction and NPS
• Use AI to improve customer support
• Use AI to improve customer upselling and churn reduction
• Use AI to detect fraud
• Use AI to predict consumption
• Use AI to predict sales volume
• Use AI to predict visitors
• Use AI to optimize cross channel ad costs
• Use AI to optimize Next Best Action
• Use AI to optimize outbound capacity
• Use AI to find next best location for branches, showrooms
Todays language is using both -AI and ML- terms interchangeably. Tech oriented people use more frequently Machine Learning whereas in common language AI is more wide spread. In past terminology Machine Learning used to be a subset of Artificial Intelligence.
ML covered development of algorithms that allow computers to learn from data and make predictions or decisions without being explicitly programmed.
AI definition was closer to human intelligence including multiple tasks such as perception, reasoning, understanding.
AI usually means “Narrow AI” that is focusing on specific task and Artificial General Intelligence (AGI) refers to machine that can perform any intellectual task that a human can.
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Deep Learning is a subset of Machine Learning algorithms. Deep learning algorithms use multiple layers of neurons similar to human brain cell structure. The number of layers defines the depth of neural network.
It depends on the business case whether deep learning algorithm or a tabular, tree based algorithm provides better results.
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This really depends on the industry dynamics. In banking model performance might not decrease significantly for up to 1-2 years, on the other hand in ecommerce the best practice is frequent or continuous training.