New Approach for Market Intelligence Using Artificial and Computational Intelligence

Small and medium sized retailers are central to the private sector and a vital contributor to economic growth, but often they face enormous challenges in unleashing their full potential. Financial pitfalls, lack of adequate access to markets, and difficulties in exploiting technology have prevented them from achieving optimal productivity. Market Intelligence (MI) is the knowledge extracted from numerous internal and external data sources, aimed at providing a holistic view of the state of the market and influence marketing related decision-making processes in real-time. A related, burgeoning phenomenon and crucial topic in the field of marketing is Artificial Intelligence (AI) that entails fundamental changes to the skillssets marketers require. A vast amount of knowledge is stored in retailers’ point-of-sales databases. The format of this data often makes the knowledge they store hard to access and identify. As a powerful AI technique, Association Rules Mining helps to identify frequently associated patterns stored in large databases to predict customers’ shopping journeys. Consequently, the method has emerged as the key driver of cross-selling and upselling in the retail industry. At the core of this approach is the Market Basket Analysis that captures knowledge from heterogeneous customer shopping patterns and examines the effects of marketing initiatives. Apriori, that enumerates frequent itemsets purchased together (as market baskets), is the central algorithm in the analysis process. Problems occur, as Apriori lacks computational speed and has weaknesses in providing intelligent decision support. With the growth of simultaneous database scans, the computation cost increases and results in dramatically decreasing performance. Moreover, there are shortages in decision support, especially in the methods of finding rarely occurring events and identifying the brand trending popularity before it peaks. As the objective of this research is to find intelligent ways to assist small and medium sized retailers grow with MI strategy, we demonstrate the effects of AI, with algorithms in data preprocessing, market segmentation, and finding market trends. We show with a sales database of a small, local retailer how our Åbo algorithm increases mining performance and intelligence, as well as how it helps to extract valuable marketing insights to assess demand dynamics and product popularity trends. We also show how this results in commercial advantage and tangible return on investment. Additionally, an enhanced normal distribution method assists data pre-processing and helps to explore different types of potential anomalies.Små och medelstora detaljhandlare är centrala aktörer i den privata sektorn och bidrar starkt till den ekonomiska tillväxten, men de möter ofta enorma utmaningar i att uppnå sin fulla potential. Finansiella svårigheter, brist på marknadstillträde och svårigheter att utnyttja teknologi har ofta hindrat dem från att nå optimal produktivitet. Marknadsintelligens (MI) består av kunskap som samlats in från olika interna externa källor av data och som syftar till att erbjuda en helhetssyn av marknadsläget samt möjliggöra beslutsfattande i realtid. Ett relaterat och växande fenomen, samt ett viktigt tema inom marknadsföring är artificiell intelligens (AI) som ställer nya krav på marknadsförarnas färdigheter. Enorma mängder kunskap finns sparade i databaser av transaktioner samlade från detaljhandlarnas försäljningsplatser. Ändå är formatet på dessa data ofta sådant att det inte är lätt att tillgå och utnyttja kunskapen. Som AI-verktyg erbjuder affinitetsanalys en effektiv teknik för att identifiera upprepade mönster som statistiska associationer i data lagrade i stora försäljningsdatabaser. De hittade mönstren kan sedan utnyttjas som regler som förutser kundernas köpbeteende. I detaljhandel har affinitetsanalys blivit en nyckelfaktor bakom kors- och uppförsäljning. Som den centrala metoden i denna process fungerar marknadskorgsanalys som fångar upp kunskap från de heterogena köpbeteendena i data och hjälper till att utreda hur effektiva marknadsföringsplaner är. Apriori, som räknar upp de vanligt förekommande produktkombinationerna som köps tillsammans (marknadskorgen), är den centrala algoritmen i analysprocessen. Trots detta har Apriori brister som algoritm gällande låg beräkningshastighet och svag intelligens. När antalet parallella databassökningar stiger, ökar också beräkningskostnaden, vilket har negativa effekter på prestanda. Dessutom finns det brister i beslutstödet, speciellt gällande metoder att hitta sällan förekommande produktkombinationer, och i att identifiera ökande popularitet av varumärken från trenddata och utnyttja det innan det når sin höjdpunkt. Eftersom målet för denna forskning är att hjälpa små och medelstora detaljhandlare att växa med hjälp av MI-strategier, demonstreras effekter av AI med hjälp av algoritmer i förberedelsen av data, marknadssegmentering och trendanalys. Med hjälp av försäljningsdata från en liten, lokal detaljhandlare visar vi hur Åbo-algoritmen ökar prestanda och intelligens i datautvinningsprocessen och hjälper till att avslöja värdefulla insikter för marknadsföring, framför allt gällande dynamiken i efterfrågan och trender i populariteten av produkterna. Ytterligare visas hur detta resulterar i kommersiella fördelar och konkret avkastning på investering. Dessutom hjälper den utvidgade normalfördelningsmetoden i förberedelsen av data och med att hitta olika slags anomalier

Market Segmentation Using Enhanced RFM (Regency, Frequency, Monetary) Model

The importance of targeted marketing strategy, a principle method for transforming retailers from being product-oriented to customer centric, has attracted interest from both industry and academia. It is well known fact that consumers differ in various ways, and have contrasting buying preferences. A widely used approach for gaining insight into the heterogeneity of customer buying behavior and profitability is market segmentation. It refers to the division of a mass market into smaller homogeneous markets based on purchase similarity and the diversity of customers

Assessment of Airway Bronchodilation by Spirometry Compared to Airway Obstruction in Young Children with Asthma

A reversibility test by an increase of greater than 12% in FEV1 can support a diagnosis of asthma and alter a patient’s treatment plan but may not be applicable to the young ages. We retrospectively gathered spirometric data from 85/271 asthmatic children having mild obstruction (FEV1 > 80% predicted), age 2.6–6.9 years. Spirometry was performed before and 20 min after inhalation of 200 mcg Albuterol. We defined a deviation below −1.64 z scores from control as obstruction and an increased above 1.64 scores from control as a positive response to bronchodilators. Sensitivity of the index was considered significant if it captured >68% of the participants. The sensitivity of detecting airway obstruction in these children by FEV1 was 15.3% and 62.4% by FEF25–75. A positive response to Albuterol was an increase of 9.2% for FEV1 (12% for adults) and 18.5% for FEF25–75. The sensitivity for detecting a response to Albuterol in mild asthma was 64.7% by FEV1 and 91.8% by FEF25–75. Young children having normal spirometry can demonstrate airway reversibility. The response of spirometry parameters to bronchodilators may be more sensitive than obstruction detection and may help to support the diagnosis of asthma and adjust treatment plan

The effect of probiotic administration on metabolomics and glucose metabolism in CF patients

Background and objectives Cystic fibrosis (CF)-related diabetes (CFRD) affects 50% of CF adults. Gut microbial imbalance (dysbiosis) aggravates their inflammatory response and contributes to insulin resistance (IR). We hypothesized that probiotics may improve glucose tolerance by correcting dysbiosis. Methods A single-center prospective pilot study assessing the effect of Vivomixx (R) probiotic (450 billion/sachet) on clinical status, spirometry, lung clearance index (LCI), and quality of life (QOL) questionnaires; inflammatory parameters (urine and stool metabolomics, blood cytokines); and glucose metabolism (oral glucose tolerance test [OGTT]), continuous glucose monitoring [CGM], and homeostasis model assessment of IR (HOMA-IR) in CF patients. Results Twenty-three CF patients (six CFRD), mean age 17.7 +/- 8.2 years. After 4 months of probiotic administration, urinary cysteine (p = 0.018), lactulose (p = 0.028), arabinose (p = 0.036), mannitol (p = 0.041), and indole 3-lactate (p = 0.046) significantly increased, while 3-methylhistidine (p = 0.046) and N-acetyl glutamine (p = 0.047) decreased. Stool 2-Hydroxyisobutyrate (p = 0.022) and 3-methyl-2-oxovalerate (p = 0.034) decreased. Principal component analysis, based on urine metabolites, found significant partitions between subjects at the end of treatment compared to baseline (p = 0.004). After 2 months of probiotics, the digestive symptoms domain of Cystic Fibrosis Questionnaire-Revised improved (p = 0.007). In the nondiabetic patients, a slight decrease in HOMA-IR, from 2.28 to 1.86, was observed. There was no significant change in spirometry results, LCI, blood cytokines and CGM. Conclusions Changes in urine and stool metabolic profiles, following the administration of probiotics, may suggest a positive effect on glucose metabolism in CF. Larger long-term studies are needed to confirm our findings. Understanding the interplay between dysbiosis, inflammation, and glucose metabolism may help preventing CFRD

Intermittent inhaled tobramycin and systemic cytokines response in CF patients with Pseudomonas aeruginosa

Introduction: CF pulmonary guidelines recommend alternate therapy (one month on, one month off) with inhaled tobramycin for chronic Pseudomonas aeruginosa colonization in cystic fibrosis (CF). Tobramycin-inhaled powder (TIP™) is increasingly replacing time-consuming nebulizer therapy. It is unclear whether laboratory parameters change during the month off period compared with the month on therapy. Purpose: Our aim was to assess whether spirometry, lung clearance index and circulating inflammatory markers differ between on/off treatment periods. Materials and methods: A prospective pilot study evaluating CF patients treated with TIP, on two consecutive months (on/off) therapy. The evaluations were performed at the end of a month off therapy (1-2 days before the initiation of TIP) and after 28 days of treatment with TIP (1-2 days after the end of the treatment cycle). Results: Nineteen CF patients (10 males) with a mean age of 18.7±9.7 years and BMI (body mass index) of 19.62±3.53 kg/m2 were evaluated. After a month off treatment with TIP, spirometry parameters and lung clearance index remained unchanged. IL-6 increased significantly (p=0.022) off treatment. There was a non-significant change in the other inflammatory cytokines off therapy [hs-CRP, IL-8,TNF-α, α1-antitrypsin (α1AT) and neutrophilic elastase]. Conclusions: The results of lung function parameters support the relative stability of CF patients during the month off therapy; however, the difference in serum IL-6 raises the possibility of ongoing higher degrees of inflammation during the month off therapy with TIP. The small sample size and the multiple parameters evaluated preclude firm conclusions; therefore, larger multicenter studies are needed to assess the on/off treatment strategy

Exercise capacity in patients with cystic fibrosis vs. non-cystic fibrosis bronchiectasis.

BackgroundBronchiectasis is associated with morbidity, low exercise capacity and poor quality of life. There is a paucity of data on exercise capacity using cardiopulmonary exercise test (CPET) in non-cystic fibrosis (CF) bronchiectasis. Our aim was to compare exercise capacity using CPET in CF and non-CF bronchiectasis patients.MethodsCross-sectional retrospective/prospective controlled study assessing CPET using cycle ergometer. Exercise parameters and computed tomography (CT) findings were compared. Results: Hundred two patients with bronchiectasis and 88 controls were evaluated; 49 CF (age 19.7 ± 9.7 y/o, FEV1%predicted 70.9 ± 20.5%) and 53 non-CF (18.6 ± 10.6 y/o, FEV1%predicted 68.7 ± 21.5%). Peak oxygen uptake (peak [Formula: see text]) was similar and relatively preserved in both groups (CF 1915.5±702.0; non-CF 1740±568; control 2111.0±748.3 mL/min). Breathing limitation was found in the two groups vs. control; low breathing reserve (49% in CF; 43% non-CF; 5% control) and increased [Formula: see text] (CF 31.4±4.1, non-CF 31.7±4.1 and control 27.2 ± 2.8). Oxygen pulse was lower in the non-CF; whereas a linear relationship between peak [Formula: see text] vs. FEV1 and vs. FVC was found only for CF. CT score correlated with [Formula: see text] and negatively correlated with [Formula: see text] and post exercise oxygen saturation (SpO2).ConclusionsCPET parameters may differ between CF and non-CF bronchiectasis. However, normal exercise capacity may be found unrelated to the etiology of the bronchiectasis. Anatomical changes in CT are associated with functional finding of increased [Formula: see text] and decreased SpO2. Larger longitudinal studies including cardiac assessment are needed to better study exercise capacity in different etiologies of non-CF bronchiectasis.Trial registrationClinicalTrials.gov, registration number: NCT03147651