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. In the dynamic world of digital marketing, staying ahead means embracing innovative technologies that drive performance and create value.
HolisticRM, as a leading machine learning consulting firm, stands at the forefront of this technological revolution. Our expertise extends from harnessing the power of AI in digital marketing to implementing sophisticated recommendation systems, all tailored to propel your brand into the future.
Choose AI around the decision you need to improve
A custom AI project can combine forecasting, ranking, image interpretation and language models. The right combination depends on the business action, available data and operating constraints. These guides explain the choices, evidence and responsibilities to agree before committing to a build.
AI development grounded in your business and our project experience
AI development connects a business decision to models your team can use and evaluate. Our banking, location and manufacturing projects show why data, model selection and delivery design must fit the task. That experience shapes our brief before an architecture is chosen.
What our development experience changes in the brief
Three assignments show why the model follows the business decision. For Cofidis Hungary, our published work describes product-affinity models connected to next-best-offer selection and outbound capacity. The useful output was an eligible contact-and-offer decision, not a score displayed in isolation. For MOL Group / Fresh Corner, location and retail-format modelling combined business questions with GeoX spatial information. Our confidential manufacturing work includes generating candidate test code from technical specifications while preserving identifiers and configurations.
These projects require different evidence. A banking offer needs eligibility and channel constraints; a location decision needs geographic transfer checks; generated engineering code needs technical validation. Our holistic method develops the data representation and modelling approach together. We compare the complete workflow against a useful baseline before retaining an extra model or data source.
Choosing an AI agency in 2026: evidence to request
Ask an AI agency for a relevant delivery, its own contribution and the acceptance evidence. Then ask who owns the interfaces, exception review and operations after the demonstration. Those answers turn a prototype into a delivery proposal.
| Ask about | Useful evidence | Insufficient alone |
|---|---|---|
| Business fit | Offer and capacity constraints, as in Cofidis work | Generic model accuracy |
| Engineering quality | Manufacturing requirements, identifiers and validation | Generated code volume |
| Own contribution | Named role and precise reference scope | A customer-logo wall |
| Operating handover | Interfaces, rights, support and acceptance | A successful demonstration |
Explore our dedicated AI agent development and data science consulting guides for those specific delivery needs.
Choose the approach that fits the decision
| Option | Useful when | Trade-off to test |
|---|---|---|
| Existing product | A standard workflow meets the requirement | Configuration limits, integration and recurring cost |
| Adapted model | A reusable model needs task-specific behaviour | Data preparation, evaluation and dependency management |
| Bespoke model set | Your data and decision create a distinct advantage | Development effort, ownership and ongoing support |
Bring a decision, its baseline and the cost of error
For an offer-allocation brief, bring eligible products, contact capacity and the current selection policy. For a location brief, bring candidate sites and exclusion rules. For engineering generation, bring approved specifications and acceptance checks. A supplier should explain which missing input prevents a useful comparison.
Compare a proposal you could actually commission
- Define the decision and the smallest useful output: an offer queue, location shortlist or reviewed engineering artifact.
- Compare a standard product, an adapted model and a bespoke design against the same required output.
- Require a sample handover: source assumptions, acceptance evidence, interfaces, costs and unresolved decisions.
- Commission a bounded pilot with a decision owner; use its evidence to stop, refine or extend the scope.
Compare evidence, not presentation quality
Sample procurement exercise: allocate 30 points to task and domain fit, 25 to evaluation evidence, 20 to integration and operating ownership, 15 to data and rights, and 10 to whole-life cost. Ask suppliers to show the evidence for each score. These illustrative weights are buyer choices, not an independent rating of HolisticRM.
Sample: an illustrative evaluation plan
Sample — illustrative scenario, not a client result. For illustration, a 200-order historical review could compare the current supplier rules with a prediction model and a document-extraction component. Freeze the information available at the decision time and permit a queue of 20 alerts per week. Count confirmed actionable delays, missed critical orders and review minutes. The numbers define a test design, not observed HolisticRM customer performance.
What you receive
A decision brief, data-readiness assessment, model-option comparison, pilot acceptance plan and integration outline. The proposal identifies ownership and licence boundaries, expected operating responsibilities and the evidence needed for a go, refine or stop decision. Compare the complete delivery scope when selecting an AI development partner.
Questions buyers ask
- Do we need an LLM?
- Only when language-related tasks justify it. Forecasting, ranking and optimisation may be better served by other model families.
- Do we own the resulting models?
- Ownership, licences, data rights and handover materials are agreed in the contract. Existing third-party components retain their applicable terms.
- Can you work with limited data?
- We assess whether rules, transfer learning, a narrower scope or further data collection provides a useful starting point.
HolisticRM brings experience across 136+ AI models deployed, combining domain data, predictive models and language technologies around specific business problems. See our published project references. Explore relevant industry applications or our AI development approach.
Discuss your custom AI project. Bring a short, non-confidential description of the decision, current process and available data.
LLM fine-tuning, retrieval or prompting: choose by task
LLM fine-tuning adapts a model using task-specific examples. It can help with specialised behaviour, formats or repeated task patterns. Retrieval supplies relevant source material at answer time; prompting supplies instructions and examples. These techniques can work together, but each should solve an observed problem rather than become a default architecture choice.
Experience: technical specifications to test-code candidates
Our published manufacturing LLM case describes multiple language models, task-specific fine-tuning, document processing and function-specific tooling for engineering test-code generation. A central requirement was preserving critical identifiers and configurations as information moved from specifications into code. The customer remains confidential, so the public explanation concentrates on the task and our engineering contribution.
This experience shapes how we scope a new LLM assignment. We distinguish a source-reading failure from an output-structure failure and an incorrect engineering inference. Each failure suggests a different remedy: better document extraction, more useful retrieved context, clearer examples, model adaptation or a deterministic check. Increasing model size cannot be assumed to solve these different failure modes.
What to compare before commissioning fine-tuning
Request an error analysis of the baseline. If the answer is missing an approved fact, examine retrieval and source access. If it repeatedly violates a stable task format despite clear instructions, adaptation may deserve a trial. If the requested output can be verified by a parser, compiler or business rule, include that check in the workflow. Compare the accepted output after correction, not just the first generated draft.
A model combination can separate document extraction, evidence retrieval, generation and validation. Each component has its own test, while the complete chain must preserve the original requirement. The objective is a useful engineering assistant with accountable outputs, rather than an impressive volume of unchecked code.
Choose the approach that fits the decision
| Option | Useful when | Trade-off to test |
|---|---|---|
| Prompting | Instructions and a few examples are sufficient | Consistency on varied and difficult inputs |
| Retrieval | Answers need current, attributable knowledge | Finding the right source and using it accurately |
| Fine-tuning | Repeated errors justify learned task adaptation | Example quality, versioning and generalisation |
Prepare specifications and acceptance examples
Bring controlled specification versions, required identifiers, accepted output examples and the checks engineers already use. Identify document sections that frequently cause errors and examples where clarification is mandatory. Training rights and confidentiality determine which material can enter adaptation or evaluation.
Isolate the source of each LLM error
- Test document extraction and identifier preservation before judging generated code.
- Compare prompting, retrieved context and adaptation on the same approved tasks.
- Use independent cases to assess requirement coverage, executable checks and correction effort.
- Release a reviewed generation workflow with recorded model, document and configuration versions.
Count accepted engineering work
Measure reviewed test cases or accepted outputs per engineering hour, including corrections and validation. Track lost identifiers, unsupported assumptions and missed requirements separately. Reusable test specifications and traceable requirement links can improve knowledge continuity; generated line count alone does not demonstrate that benefit.
Sample: an illustrative evaluation plan
Sample — illustrative scenario, not a client result. For illustration, an agreed 100-case test set could contain 60 routine specifications, 25 difficult variants and 15 cases requiring clarification. Compare prompting, retrieval-assisted generation and adaptation on preserved identifiers, executable checks and reviewer correction time. Keep the final set separate from development. These are illustrative evaluation counts, not a historical manufacturing result.
What you receive
An error taxonomy, versioned evaluation set, document and example specification, model-option comparison and a reviewed output workflow. The handover identifies training-data rights, retrieval sources, model dependencies, validation checks and operating cost. Confidential client examples are not reused without permission.
Questions buyers ask
- Does fine-tuning keep knowledge up to date?
- Not automatically. Frequently changing information often needs an approved retrieval or tool-access path, with source ownership and refresh rules.
- Can fine-tuning remove hallucinations?
- It can address particular errors, but cannot guarantee factual output. Source checks, constrained outputs and review remain task-dependent controls.
- Must we use one model for everything?
- No. A smaller classifier, a retrieval component and a generation model can perform different parts of the workflow when the combination passes evaluation.
Read our manufacturing LLM reference, which describes work on technical test-code generation. The client remains confidential. Explore relevant industry applications or our AI development approach.
Discuss your LLM project. Bring a short, non-confidential description of the decision, current process and available data.
Computer vision connected to the operating environment
Computer vision extracts useful information from images or video. A project may detect an object, classify a condition, measure a feature or prioritise images for review. Its value depends on the capture environment and the decision made from the result. We evaluate the complete acquisition-to-action workflow, including image quality and uncertain cases.
Experience: visual signals alongside location and business data
Our public project portfolio describes image classification and deep-learning work using TensorFlow and convolutional neural networks in a geographic context. The ESA BeSpatial.AI demonstration record separately describes a completed service combining Earth-observation, geodemographic and customer-location information, with HolisticRM as prime contractor. This is evidence of visual and spatial modelling experience; it is not a claim that every factory-inspection opportunity below has already been delivered.
The transferable engineering question is whether a visual feature improves the decision once relevant domain context is included. In location work, imagery alone cannot describe every commercial constraint. In a proposed manufacturing inspection, a surface pattern may have different meaning across product variants, materials or camera conditions. We design evaluation around those distinctions rather than treating every image as interchangeable.
Choose the capture-and-model combination
Compare three practical options: improve the image-capture process, adapt a suitable vision model, or combine its output with trusted structured data. Better lighting or a more consistent view may solve a failure that retraining cannot. Conversely, a visually ambiguous case may require product metadata or a reviewer instead of an automatic decision.
Ask for separate results by operating condition, including rare defects and unfamiliar backgrounds. A single overall score can hide a failure on the cases that matter most. The right package includes an annotation specification, error examples and a review workflow, not only model weights.
Choose the approach that fits the decision
| Option | Useful when | Trade-off to test |
|---|---|---|
| Image classification | A whole image needs a defined category | Ambiguous labels and changing backgrounds |
| Detection or segmentation | Location, shape or count matters | Annotation effort and missed small features |
| Vision plus business data | Context changes the interpretation | Data alignment, missing inputs and added complexity |
Start with the image and its operating context
Supply representative images, capture conditions, label definitions and examples where reviewers disagree. In Earth observation, include source dates and spatial context; in a proposed inspection, include product variants, lighting and camera setup. Confirm permitted use and retention before building the evaluation set.
Test capture, interpretation and review together
- Check that the relevant feature is visible and that expert labels are consistent.
- Compare capture improvements, a suitable vision model and useful domain-data enrichment.
- Separate evaluation by relevant location, batch, sensor or period rather than near-identical images.
- Pilot the review queue with explicit treatment of uncertain inputs and operational overrides.
Measure useful detections under real conditions
Report missed target cases and unnecessary reviews by condition, with time spent resolving uncertainty. In spatial work, test whether the visual signal improves the location decision beyond existing data. Consistent evidence and annotation definitions help specialists discuss results; they do not establish an automatic operational saving.
Sample: an illustrative evaluation plan
Sample — illustrative scenario, not a client result. For illustration, agree a review set of 300 images across three relevant capture conditions, with defect categories and rare cases deliberately represented. Keep related frames or batches together when separating development from evaluation. Count missed defects and unnecessary inspections by condition. The sample size and distribution need expert agreement; these figures are not an achieved accuracy claim.
What you receive
A capture and annotation specification, representative evaluation set, model comparison, condition-specific error report and integration plan. Agree confidence handling, review capacity and the fallback before using predictions in operations. Ownership and permitted image use are documented alongside technical delivery.
Questions buyers ask
- Can existing cameras be used?
- Possibly. We first check resolution, field of view, stability and whether the required features are visible under normal operating conditions.
- How many labelled images are needed?
- The requirement depends on task variation, rarity and acceptable errors. A representative feasibility sample is more useful than an unsupported fixed minimum.
- Can images be combined with other data?
- Yes, where timestamps and entities can be aligned. We test whether operational or geospatial context adds value beyond the image-only approach.
Our space and Earth-observation portfolio shows how visual and domain information contribute to broader modelling work. We bring that practice of testing complementary data sources to the evaluation of your application. Explore relevant industry applications or our AI development approach.
Discuss your computer vision project. Bring a short, non-confidential description of the decision, current process and available data.
Recommendation models for relevant customer decisions
Recommendation models help select relevant products, content or next actions from an eligible set. The commercial objective should include suitability, availability and the customer journey. We connect ranking models with business rules and evaluate whether the resulting experience changes useful outcomes, rather than assuming that more clicks represent more value.
Experience: Cofidis offers and NetPincér discovery
For Cofidis Hungary, our published project describes separate product-affinity models, eligibility scoring and a best-mix approach for outbound contact capacity. The assignment connected prediction to which eligible offer could be presented through an appropriate channel. Its published technology description includes logistic regression, boosted models and ensembles; the business policy remained an essential part of the system.
Our historical NetPincér portfolio entry describes response modelling and a recommendation engine using Azure Matchbox with GeoX location context, plus automated learning and scoring. This identifies the historical implementation, not a recommendation to copy that stack into a new project. Microsoft’s 2017 coverage also discussed NetPincér’s recommendation service during Satya Nadella’s regional visit; it does not name HolisticRM as supplier.
Choose a recommender by the decision it serves
Food discovery, a bank offer and a retail cross-sell have different candidate sets and constraints. Compare models only after defining what may be recommended, the available context and how an accepted outcome will be measured. A behaviour-based model may help repeat visitors; product attributes or session context can support cold starts. An allocation layer can respect finite call-centre or campaign capacity.
Our experience favours this complete decision design: improve product and customer data while refining the ranking, then test the combined selection policy. Recommendation quality, contact pressure, suitability and incremental value belong in the same evaluation.
Choose the approach that fits the decision
| Option | Useful when | Trade-off to test |
|---|---|---|
| Content-based matching | Product attributes matter or history is limited | Attribute quality and limited discovery |
| Behaviour-based ranking | Interaction patterns provide a useful signal | Cold starts, popularity bias and feedback effects |
| Contextual next-best-action | Timing, channel or eligibility changes the choice | Decision complexity and reliable context data |
Specify the eligible candidate set and actual outcome
For offers, bring eligibility, contact limits and channel rules. For ecommerce, bring catalog attributes, stock, events and returns. Identify what was shown to whom: historical purchases without exposure information can hide why alternatives were never selected.
Evaluate the selection policy, not only the ranking
- Build an eligible-candidate baseline and define the customer outcome.
- Compare behaviour, content and session signals with relevant allocation rules.
- Test new customers, unavailable items and contact-capacity constraints before a live experiment.
- Measure incremental results with agreed guardrails and maintain a usable fallback as offers change.
Follow value beyond the click
For a bank, inspect accepted eligible offers, contact effort and complaints. For a store, include contribution after returns and fulfilment. Measure whether the policy helps customers find a suitable next step. A high-response group can be commercially attractive without proving the contact created additional demand.
Sample: an illustrative evaluation plan
Sample — illustrative scenario, not a client result. For illustration, assign eligible traffic to the existing and proposed ranking for a pre-agreed period. A planning worksheet can use 10,000 eligible sessions as a provisional scenario, then calculate whether the actual baseline and expected effect justify that sample. Track contribution after returns and fulfilment alongside conversion. The session count is illustrative, not a power guarantee or a reported customer result.
What you receive
A candidate and eligibility specification, event-data assessment, reference ranking, model comparison and pilot measurement plan. Handover covers the serving interface, fallbacks, allocation rules and outcome monitoring. The commercial review includes what happens after the click, accepted offer or customer contact.
Questions buyers ask
- Can this work for new customers?
- Yes. Session signals, product attributes and a tested fallback can support a useful experience before a long history exists.
- Can recommendations support a call centre?
- Yes. Offer ranking can be combined with eligibility, channel capacity and contact policies. The team’s actual operating constraints shape allocation.
- Will a recommendation model create demand?
- It may influence choices, but prediction and causal impact are different. Controlled measurement is needed to estimate additional business value.
See our Cofidis next-best-offer reference and the wider recommendation portfolio. Explore relevant industry applications or our AI development approach.
Discuss your recommendation project. Bring a short, non-confidential description of the decision, current process and available data.
Increase Cross-Sell efficiency with Machine Learning (AI)
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.
AI based 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, 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.
Custom Machine Learning Models in weeks
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.
Understanding Business Goals to be fulfilled with achieved with AI
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.
Data Processing for ML
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?)
AI Model Building
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.
AI Model Evaluation
Depending on the ML use case we do validate the models on multiple levels before we let the model published for test campaign.
Deployment into production
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.
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.
Recommendation Engine Types
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.
NBO Recommendation Engine
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.
Power of custom Recommendation Engine
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.
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 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.
Leveraging Machine Learning in Micro Location Analysis
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.
Big Data and Machine Learning in digital marketing
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: info@holisticrm.com
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.
Marketing with AI: A Strategic Revolution
Integrating AI in digital marketing is no longer an option; it’s a necessity. Through advanced AI ML consulting, HolisticRM assists businesses in navigating the AI marketing landscape, ensuring they capitalize on algorithms, data analytics, and machine learning techniques to achieve a competitive edge. Our strategies are designed to resonate with your brand identity and your audience’s evolving needs.
Harnessing the Power of AI Recommendation Systems
At the heart of personalized marketing lies the recommendation engine. Our state-of-the-art AI recommendation system utilizes machine learning to analyze patterns, predict preferences, and deliver bespoke suggestions to your audience. With options for both online recommendation engines and more integrated solutions, we ensure that your services are not just seen, but resonate on a personal level.
AI Performance Marketing: Efficiency Meets Effectiveness
As a performance marketing agency, we understand the importance of measurable success. AI performance marketing takes this a step further by optimizing campaigns in real-time, ensuring your investment translates into tangible results. AI’s analytical prowess means more effective targeting, budget allocation, and higher ROI, setting us apart as a high-caliber performance agency.
AI for X-Sell and Up-Selling: Maximizing Revenue Opportunities
AI’s predictive abilities redefine strategies for both cross-selling and up-selling. ‘AI for X-Sell’ utilizes customer data to present relevant additional offerings at the right moments, enhancing the customer experience and increasing sales. Simultaneously, intelligent up-selling techniques ensure customers are exposed to premium options they’re likely to adopt, significantly driving up order values.
Machine Learning impact on X–Sell
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.
AI Customer Experience Optimization
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.
Machine Learning Customer Experience Optimization Use-Cases
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.
AI Campaign: The Smart Approach to Digital Strategy
Launching an AI campaign involves more than just setting parameters; it’s about employing advanced machine learning consulting expertise to craft self-adaptive, intelligent campaigns. These initiatives consider user behavior, market trends, and predictive analytics, adjusting in real time to enhance performance continuously.
AI Digital Marketing Strategy: Shaping the Future of Engagement
Our holistic AI digital marketing strategy encompasses all facets of your online presence. From AI advertising that delivers compelling, targeted content to harnessing AI for marketing analytics and decision-making, our strategies are comprehensive, innovative, and future-focused.
The AI Marketing Company You Can Trust
As an AI marketing company, we don’t just follow trends; we set them. Our commitment to AI innovation in every aspect of marketing—from performance and advertising to cross-selling and online recommendation engines—establishes us as leaders in the field.
Embracing AI with HolisticRM means more than keeping up; it means leading the charge in the digital marketing revolution. Our multifaceted approach—combining machine learning, data analytics, personalized strategies, and continual adaptation—positions your brand precisely where it needs to be: at the forefront of your industry. Dive into the future of marketing; contact us to redefine what’s possible for your business.
AI marketing for Retail and eCommerce
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.
Artificial Intelligence for Banking
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.
Further HolistiCRM use-cases for AI
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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It depends on various factors such as data complexity, quality, domain complexity, but we can build recommendation or next best offers models under 10k EUR.
We developed Netpincer model for less than 8k EUR.
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It depends on various factors such as data complexity, quality, domain complexity, but we can build recommendation or next best offers models in 6-18 weeks.
We developed Netpincer model in less than 2 month. Since than we have enhance our engines contact us for more details.
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.