SmarterXTM
We build smarter, next-gen businesses.
SmarterX Consulting uses the Problem-Based Model (as featured in Marketing Artificial Intelligence: AI, Marketing and the Future of Business) to reduce costs, accelerate revenue growth, and build smarter businesses.
We leverage our relationships with hundreds of AI vendors, dozens of AI experts, and decades of consulting experience, to deliver innovative solutions that drive more intelligent digital transformation within your organization.
In this model, the client has a known pain point, a challenge that they believe may be solved more efficiently, and at scale, with AI.
For example: The Company has more than 100,000 email addresses, and sent more than X,XXX,XXX emails in the last 12 months, but open rates remain low at X%, and conversions attributed to email have remained flat since 2018.
Based on current averages, a 2 percentage point lift in email open rates could produce an $XXX,XXX increase in revenue over the next 12 months.
Our team follows a 10-step process to build the solution. The ideal problem-based consulting projects have a minimum $100,000 year-one value to solve, with a minimum 3 - 5x ROI potential in three years.
The project is broken into two phases:
- Discovery: Define and validate the problem.
- Planning: Establish the strategy to resolve it.
Discovery Phase
1. Define the problem statement.
- What is the challenge that will be solved?
- The problem statement is drafted by the Institute based on discovery calls and meetings.
- Once the client signs off on the problem statement, it becomes the foundation of the consulting engagement.
- SAMPLE Problem Statement: The Company has more than 100,000 email addresses, and sent more than X,XXX,XXX emails in the last 12 months, but open rates remain low at X%, and conversions attributed to email have remained flat since 2020. Based on current averages, a 2 percentage point lift in email open rates could produce an $XXX,XXX increase in revenue over the next 12 months.
- Determine the estimated value of solving the problem. This helps make the business case for the time and resources needed to build the solution.
2. Build and prioritize the issues list.
- What are the primary issues causing the problem?
- The issues are categorized into 3 - 5 primary groups and built out into an issues tree.
- SAMPLE Preliminary Issues List:
- Low open rates
- Low click rates
- Low sales conversion rates
3. Identify and prioritize the key drivers.
- What factors are driving the issues and problem?
- SAMPLE Preliminary Key Drivers:
- Decentralized data
- Duplicate contacts
- List fatigue
- Email creative
- Highly manual, human-driven processes
- Underutilized or missing marketing technology solutions
- Lack of list segmentation
- Lack of reporting and performance management
4. Develop an initial hypothesis.
- What is the preliminary roadmap to solving the problem?
- SAMPLE Preliminary Initial Hypothesis: By centralizing the Company’s data, and unifying the marketing technology stack, AI-powered technologies can be integrated to intelligently automate priority use cases that will drive efficiency and performance.
5. Conduct discovery research.
- What is the value of solving the problem?
- How are talent, technology and strategy gaps impacting performance?
- What can be learned from interviews with stakeholders and secondary research related to the problem?
- What is the current understanding of AI within the organization?
- Does the leadership team understand and support the goal of AI pilot projects?
- What are internal capabilities related to data and AI?
- What were the primary KPIs and goals for the last 12 months?
- What are the current processes for solving the problem?
- How is performance being monitored and reported?
- How can we benchmark goals moving forward?
- What are industry benchmarks and best practices?
- What opportunities exist to create a competitive advantage?
- What technologies are being used?
- Review current tools and processes to address the business challenge.
- Document existing tech stack, including costs, capabilities and utilization.
- What is the structure and quality of data (if applicable)?
- Document current data flow and management.
- Assess data quality and feasibility for AI applications.
- Identify opportunities to enrich and improve current data resources.
- Identify relevant machine learning methodologies.
- Identify gaps in data to make recommended techs/solutions work.
- What latent assets, specifically structured and unstructured data, can be used?
- What are the data rules, policies, standards and models that govern data collection, storage, management and integration?
- How will the success of AI-powered solutions be measured?
- Is the goal of an AI solution focused on cost reduction, increased revenue generation/performance, or both?
6. Validate issues and drivers.
- Does discovery research validate the initial issues and drivers?
- Has anything been learned that alters the issue tree and key drivers?
Planning Phase
7. Analyze options and build a solutions matrix.
- Assess build vs. buy options. Should the organization consider building its own solutions, or buy and integrate third-party technologies?
- Identify and analyze existing third-party AI-powered solutions.
- Develop a cost and scope to build a proprietary ML-based solution to solve the problem (if applicable).
- Develop a roadmap to self-implementing the proposed machine learning methodology (if applicable).
- The solutions matrix will focus on technologies that give the organization the ability to reduce costs through increased efficiency, and/or improve performance through smarter technology and processes.
- Which vendors offer AI-powered solutions for the use cases?
- How do the vendors compare on features, pricing, industry specialization, API access, customer support, product roadmap, funding/financial stability, integration/compatibility with existing tech stack, security and more?
- Are there options to unlock new features and capabilities within the existing marketing technology stack?
- Which emerging AI vendors offer solutions to solve the problem?
- Note, in some cases, AI may not even be necessary to improve processes and performance. This will be analyzed and surfaced as part of the project.
8. Synthesize findings.
- What has been learned in the process?
- What insights will guide recommendations?
- Develop detailed strategic briefs for each vendor, including: use cases it applies to, pricing, funding, customer ratings, and all information needed to secure approval and budget for implementation.
9. Develop recommendations.
- What actions should the client take moving forward to address and solve the problem
- How will the client monitor progress of the implementation?
10. Present final report and implementation plan.
- What are the actions, costs, implementation timelines and expected outcomes/ROI?
The end deliverable is a final report with key findings and recommendations, and an implementation plan that outlines recommended tools to address problems, and details the projected roadmap, timelines, milestones, goals, and costs.
The standard rate for a SmarterX Consulting engagement starts at $100,000 and is completed in an estimated 3-6 month period. Actual timing and cost vary based on the project scope. Below is a sample three-month timeline.
Want to Do It Yourself? Get Problem-Based Model Training On-Demand.
The SmarterX Consulting team is built to solve high-value challenges for mid-market and large enterprises. But, small businesses and startups can learn the same Problem-Based Model we use as part of our Piloting AI for Marketers online course.
Register today for only $499 and explore the framework, technologies, and templates that can fuel your transformation to a dominant next-gen business in your industry.