Holistic Machine Learning
Holistic Approach applies not just for customer management strategy, it also applies to execution and delivery. Good strategies are materialized into execution phase in data-warehouse, software development, spacial data enrichment and marketing materials. These requires specialized knowledge and we partnered with best in class partners in their field.
Increase Cross-Sell efficiency with Machine Learning
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 machine learning models- are between 1%- 5% depending on industry.
One of the most common goal of our Machine Learning 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. T
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 machine learning results into their core BAU processes via automating entire process.
Next Best Offer
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 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.
Machine Learning on site Recommendations
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 machine learning 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.
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.
Machine Learning based Churn Models & Attrition Models
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.
Machine Learning in Location Based Offers, Geo-targeting
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 machine learning approach helped us to predict which customers will visit which bank branch how many times just within 6 days.
Machine learning 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.
Leveraging Machine Learning in Micro Location Analysis
On top of location based targeting with GIS and Machine learning 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.
Big Data and Machine Learning
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 Machine Learning.
Contact us for more details: firstname.lastname@example.org
Our consultants have decades of cumulative experience in machine learning, we have built 100+ machine learning models across multiple industries from banking to e-commerce across multiple geographies.
Machine Learning Projects Frequently Asked Questions
What is the cost of Machine Learning model?
What is the timeline of a machine learning model building project?
What is the shelf life of a Machine Learning Model?
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.