Holistic Digital Campaign Management

We focus on campaign targets such as brand awareness, traffic or conversions. With good campaign setup we can increase the revenue with target ROAS bid strategy or website traffic with optimazation to clicks. We setup the campaigns on Google, Meta platforms or LinkedIn depending on business demands and campaign types.

Holistic Digital Campaign Planning

We start with discussions about campaign goals, key values, unique selling proposition, messages, goals and KPIs.
Based on received inputs we create a proposed campaign plan.

Ads (PPC) Campaign Execution

We discuss the campaign execution plan with the customer and after approval set up the campaigns.

We make sure all prerequisites are set. SEO, page conversion optimization, ads accounts, performance measurement, marketing automation activities are all in sync to support business long-term goals and short-term objectives.

While in most of cases the creatives are provided by the customer, we can work together with 3rd party creative agency.

CRM Campaign Execution

Best if we can manage campaigns holistically across customer lifecycle. Lead management nurturing is important part of customer journey boosts conversion rates significantly. We make sure that customer journey is covered with Lead management and CRM campaigns beside managing PPC campaigns.

We have hands-on experience with largest CRM platforms such as Salesforce, HubSpot, Sailthru.

Digital Campaign Monitoring

During the campaign period we keep tabs on campaigns and suggest budget increase / decrease or reallocate the budget across the campaigns if it is needed.

On demand we develop real-time holistic dashboard in Google Data Studio, PowerBI, Tableau.

If some creatives performances are low, we ask new ones and change them.

In case of search campaign, we suggest new keywords (discussing with the customer) or remove those ones which are not performing well. We take into consideration the competitors, so we check the auction insights and give suggestions based on that.
After the campaign period we create campaign evaluation of the campaign performance.

If the campaign is running continuously, we give campaign report to the customer in every month about the previous month performance.

Machine Learning Digital Funnel 

An AI digital funnel is a machine learning based technique that helps you automatically target and engage potential customers through personalized content. By automatically analyzing customer behavior, the digital funnel can identify the best time to reach each customer with relevant content. Additionally, the digital funnel can also help you segment your customers based on their behavior, so you can send them more targeted content in the future.

Digital Funnel performance optimization with AI 

Digital marketing teams can use machine learning to automatically identify the best acquisition channels and strategies for their business. By analyzing past customer behavior, machine learning can predict which channels are most likely to result in a conversion and optimize the acquisition funnel accordingly. This allows businesses to focus their acquisition efforts on the channels that are most likely to result in customers, saving time and money.

Deep learning models for customer management

1. Sentiment analysis: A deep learning model can be used to analyze customer sentiment from reviews, social media, and survey responses. This can be used to identify areas of improvement for the company, or to target marketing and customer service efforts.

2. Customer segmentation: A deep learning model can be used to segment customers based on their purchase history, demographics, and other data. This can be used to customize marketing and sales efforts, or to improve customer service.

3. Next-best-action prediction: A deep learning model can be used to predict what the next best action is for a customer, based on their past behavior and current context. This can be used to automate customer interactions, or to improve customer service.

4. Churn prediction: A deep learning model can be used to predict which customers are likely to churn, based on their past behavior. This can be used to prevent customer attrition, or to target retention efforts.

AI models for hyper personalization

Deep learning models can be used for hyper personalization in a number of ways. For example, they can be used to create personalized recommendations, to segment customers, or to target ads.

1. Personalized recommendations: Deep learning models can be used to create personalized recommendations for customers. For example, a model can be trained on a customer’s past purchase history to make recommendations for what they should buy in the future.

2. Segmentation: Deep learning models can be used to segment customers into different groups. This can be useful for targeted marketing campaigns. For example, a model could be trained to segment customers based on their demographics, interests, or behavior.

3. Targeted ads: Deep learning models can be used to target ads to specific customers. For example, a model could be trained to target ads based on a customer’s demographics, interests, or behavior.